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href=\"http://github.com/snowflakedb/ArcticInference\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EArctic Inference\u003C/a\u003E changes that. Built by Snowflake AI Research, it’s an open source vLLM plugin that brings Snowflake’s inference innovations to the community, delivering the fastest, most cost-effective open source inference for enterprise AI (see Figure 1).&nbsp;\u003C/p\u003E\r\n\u003Cp\u003EAt the core of Arctic Inference is \u003Cb\u003EShift Parallelism\u003C/b\u003E, a new parallelism strategy designed to dynamically adapt to real-world challenges and unpredictable traffic, simultaneously achieving maximum speed (lowest time to first token and time per output token) and high cost efficiency (high throughput) in a single deployment.&nbsp;\u003C/p\u003E\r\n\u003Cp\u003EIn this blog post, we’ll dive into Shift Parallelism and how the full suite of innovations in Arctic Inference (cutting-edge speculative decoding, compute reduction with SwiftKV and optimized embedding inference) advance the state of the art for real-world enterprise AI.\u003C/p\u003E\r\n\u003Ch3\u003EReal-world results, one deployment\u003C/h3\u003E\r\n\u003Cp\u003EFor real-world generative AI workloads, Arctic Inference+vLLM in a single deployment, achieves:\u003Cbr\u003E\r\n\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E3.4x faster request completion and 1.06x higher throughput compared to state-of-the-art (SoTA) throughput-optimized deployment\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E1.7x higher throughput and 1.28x faster request completion compared to SoTA latency-optimized deployment\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003EThe elusive trifecta: 2.25x lower response time, 1.75x faster generation and on-par throughput compared to bespoke deployments optimized for each metric\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EFor non-generative AI workloads, such as embeddings, Arctic Inference+vLLM delivers a whopping 1.6M toks/sec per GPU:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E16x faster than vLLM on short sequences and 4.2x faster on long sequences\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E2.4x faster than Text Embeddings Inference (TEI) on short sequences and at parity for long sequences\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EThe performance claims are supported with detailed evaluation results later in the blog post. More importantly, they’re already delivering real-world impact in production with Arctic Inference powering key workloads in Snowflake Cortex AI\u003Csup\u003E1\u003C/sup\u003E.&nbsp;\u003Cbr\u003E\r\n\u003Cbr\u003E\r\nToday we’re excited to open source Arctic Inference and Shift Parallelism for the broader AI community. If you'd like to read more or cite this work, check out our&nbsp;\u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://arxiv.org/abs/2507.11830\" target=\"_blank\"\u003Epublished paper\u003C/a\u003E.\u003Cbr\u003E\r\n&nbsp;\u003C/p\u003E\r\n\u003Ch2\u003EWhy today’s inference systems fall short\u003C/h2\u003E\r\n\u003Cp\u003EInference workloads are not like training. While training workloads are homogeneous across batches, real-world inference traffic is highly dynamic, experiencing bursty, unpredictable patterns. Furthermore, while training is throughput driven, real-world inference workloads care for three distinct metrics:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ETTFT\u003C/b\u003E (time to first token): fast initial response\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ETPOT\u003C/b\u003E (time per output token): fast full generation\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EThroughput\u003C/b\u003E: cost-efficient token serving at scale\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EExisting parallelism strategies such as tensor parallelism and data parallelism were originally designed for the homogeneous, batch-optimized world of training. In real-world inference, this means significant trade-offs are introduced.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_2059071170":{"id":"blog-text-2ab3452951","text":"\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth style=\"min-width: 180px;\"\u003EStrategy\u003C/th\u003E\r\n\u003Cth\u003EStrengths\u003C/th\u003E\r\n\u003Cth\u003EWeaknesses\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd style=\"min-width: 180px;\"\u003ETensor parallelism\u003C/td\u003E\r\n\u003Ctd\u003ELeverages aggregate compute and memory across GPUs to process individual tokens\u003Cbr\u003E\r\nGreat for fast generation (low TPOT)\u003C/td\u003E\r\n\u003Ctd\u003ERequires allreduce communication per token, scaling linearly with token length (O(n)).\u003Cbr\u003E\r\nLow throughput on large batches due to large communication cost.\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd style=\"min-width: 180px;\"\u003EData parallelism\u003C/td\u003E\r\n\u003Ctd\u003EParallelizes across request boundaries with near-zero inter-GPU communication.\u003Cbr\u003E\r\nScales very well with excellent throughput on large batches.\u003C/td\u003E\r\n\u003Ctd\u003ECannot speed up work within a single request.\u003Cbr\u003E\r\nUnsuitable for highly interactive workloads due to slow TTFT and generation speed for individual requests.\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_2054978886":{"id":"blog-text-53ddacb060","text":"\u003Ch3\u003EWhy not combine them?\u003C/h3\u003E\r\n\u003Cp\u003ESwitching between tensor parallelism and data parallelism may seem obvious, but in practice, it's not viable. Their KV cache memory layouts are incompatible, and switching requires expensive data movement. Most teams resort to duplicating deployments: one for latency, one for throughput. This adds cost and complexity.\u003C/p\u003E\r\n\u003Cp\u003ETo overcome these KV cache limitations, Arctic Inference introduces a new strategy: \u003Cb\u003EArctic Sequence Parallelism\u003C/b\u003E (referenced as Arctic Ulysses in charts below).\u003C/p\u003E\r\n\u003Cp\u003EArctic Sequence Parallelism splits the input sequence across GPUs to parallelize work within a single request. Unlike tensor parallelism, it avoids costly token-wise communication (O(n)), while still achieving high GPU utilization. And because it shares a KV cache layout with Tensor parallelism, it’s the ideal counterpart for large-batch scenarios. See our previous \u003Ca href=\"https://www.snowflake.com/en/engineering-blog/ulysses-low-latency-llm-inference/\"\u003Eblog\u003C/a\u003E to learn more.&nbsp;\u003C/p\u003E\r\n\u003Cp\u003EWith Arctic Sequence Parallelism in place, this means we can finally unify the best of both worlds.\u003Cbr\u003E\r\n&nbsp;\u003C/p\u003E\r\n\u003Ch2\u003EIntroducing Shift Parallelism: One deployment, without the trade-offs&nbsp;\u003C/h2\u003E\r\n\u003Cp\u003EUnlike traditional parallelism approaches that statically optimize for one of the three inference metrics — \u003Ci\u003Eresponse latency, generation speed or cost efficiency\u003C/i\u003E — Shift Parallelism dynamically adapts based on real-world traffic, delivering all three without requiring multiple deployments tuned for each.&nbsp;\u003C/p\u003E\r\n\u003Cp\u003EShift Parallelism works by shifting between two best-in-class modes (see Figure 2):\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ETensor parallelism\u003C/b\u003E (TP) for small batches — maximizing output token generation speed (lower TPOT)\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://github.com/snowflakedb/ArcticInference/tree/main/projects/ulysses\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003E\u003Cb\u003EArctic Sequence Parallelism\u003C/b\u003E\u003C/a\u003E (SP) for large batches — minimizing TTFT and achieving near-optimal throughput\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_718442723":{"id":"image-7e0c1954f0","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--942d95e4-4084-4a16-bbd2-c896f53a1e80/shiftpar2.png?preferwebp=true&quality=85","alt":"Figure 2: Shift Parallelism shifts between two modes: tensor parallelism and sequence parallelism (Arctic Ulysses).","lazyEnabled":true,"height":"910","width":"1600","title":"Figure 2: Shift Parallelism shifts between two modes: tensor parallelism and sequence parallelism (Arctic Ulysses).",":type":"snowflake-site/components/image"},"blog_text_361688486":{"id":"blog-text-33fe0e5eb8","text":"\u003Cp\u003EThis is possible because the KV cache memory layout remains invariant between TP and SP, allowing Shift Parallelism to switch modes seamlessly, based on batch size and traffic patterns. More specifically, the KV cache layout does not change when changing SP and TP, as long as SPxTP equals P.&nbsp;\u003C/p\u003E\r\n\u003Cp\u003EThis is shown concretely in Figure 2, where Shift Parallelism can switch between TP=2 and SP=2 (Arctic Ulysses) seamlessly across forward passes due to the KV Cache Invariance. The computation shown above is a single transformer layer with four attention heads running on two GPUs. In both TP=2 and SP=2, each GPU is computing two out of four attention heads. The computation and data mapping for the attention is identical across both TP and SP, allowing Shift Parallelism to switch between the two based on the size of the input.\u003C/p\u003E\r\n\u003Cp\u003EFurthermore, by carefully mapping tensor parallel ranks to GPUs, we can ensure that the small parameter shards required on a GPU when using a large TP (TP=8, for example) are already part of the larger parameter shards present in that GPU needed to support a large SP (SP=8, for example).\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EThe result: \u003C/b\u003Ea single deployment that optimizes simultaneously for TTFT, TPOT and combined throughput — mitigating the costly trade-offs that limit traditional inference systems (see Figure 3).\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1569762019":{"id":"image-e790b020c2","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--c77e9dbb-a7c0-4316-a6ac-03aa827ae16b/shiftpar3.jpg?preferwebp=true&quality=85","alt":" Figure 3: Latency vs. throughput trade-off between forms of parallelism for Llama 3.3 70B on 8xH200 GPUs. ","lazyEnabled":true,"height":"467","width":"640","title":"Figure 3: Latency vs. throughput trade-off between forms of parallelism for Llama 3.3 70B on 8xH200 GPUs. ",":type":"snowflake-site/components/image"},"blog_text_1538335824":{"id":"blog-text-6fabb2df27","text":"\u003Cp\u003EWith Shift Parallelism, enterprises are no longer forced to choose between a latency-optimized or throughput-optimized deployment. Table 1 summarizes the latency-vs.-throughput trade-offs of the existing parallelism strategies discussed above and how Shift Parallelism mitigates them.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_1763326756":{"id":"image-246afd17eb","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a5335853-98ce-4d12-9bc0-905aa2d8b7a1/shiftpartable1.png?preferwebp=true&quality=85","alt":"Table 1: Latency vs. throughput trade-off between forms of parallelism (based on Figure 3). ","lazyEnabled":true,"height":"558","width":"1600","title":"Table 1: Latency vs. throughput trade-off between forms of parallelism (based on Figure 3). ",":type":"snowflake-site/components/image"},"blog_text_2130100425":{"id":"blog-text-e7569cb082","text":"\u003Ch2\u003EHow Arctic Inference addresses real-world enterprise inference challenges\u003C/h2\u003E\r\n\u003Cp\u003EBeyond Shift Parallelism, Arctic Inference includes a suite of advanced optimizations that target critical bottlenecks in enterprise AI workloads — from decoding and prefill inefficiencies to underoptimized embedding inference.\u003C/p\u003E\r\n\u003Cp\u003EBelow, we highlight how Arctic Inference solves key real-world challenges, with links to deeper technical blogs and papers.\u003Cbr\u003E\r\n&nbsp;\u003C/p\u003E\r\n\u003Ch3\u003EAdvancing speculative decoding for real-world generation&nbsp;&nbsp;\u003C/h3\u003E\r\n\u003Cp\u003EExisting speculative decoding solutions are limited when it comes to real-world use: They do not leverage repetitive patterns in LLM generation; they lack optimized system implementations; and draft models such as EAGLE in vLLM and sGLANG do not support inputs longer than 4,000 tokens, making them impractical.\u003C/p\u003E\r\n\u003Cp\u003EArctic Inference delivers the fastest speculative decoding in vLLM by combining suffix decoding and highly optimized light weight draft models, targeting repetitive and not repetitive generation patterns for real-world use cases. The result is up to \u003Ci\u003E4x faster generation for agentic workloads\u003C/i\u003E (with repetitive patterns) and \u003Ci\u003E2.8x faster generation for conversational and coding workloads\u003C/i\u003E (without repetitive patterns). \u003Ca href=\"https://www.snowflake.com/en/engineering-blog/fast-speculative-decoding-vllm-arctic/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ERead more\u003C/a\u003E on how this works.\u003Cbr\u003E\r\n&nbsp;\u003C/p\u003E\r\n\u003Ch3\u003ESolving redundant prefill computation with SwiftKV\u003C/h3\u003E\r\n\u003Cp\u003EIn enterprise workloads, prefill often accounts for over 90% of total compute. Yet open source frameworks such as vLLM, sGLANG and TRT-LLM lack the optimizations to reduce this cost — leading to wasted resources on long inputs with minimal output.\u003C/p\u003E\r\n\u003Cp\u003ESwiftKV reuses hidden states from earlier transformer layers to eliminate redundant computation during KV cache generation — reducing prefill compute by up to 50% without compromising accuracy. This results in up to \u003Ci\u003E2x higher throughput \u003C/i\u003Efor enterprise workloads with long prompts. To learn more about SwiftKV, please see our \u003Ca href=\"https://arxiv.org/abs/2410.03960\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Epaper\u003C/a\u003E and \u003Ca href=\"https://www.snowflake.com/en/engineering-blog/swiftkv-llm-compute-reduction/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Eblog post\u003C/a\u003E on the topic.\u003Cbr\u003E\r\n&nbsp;\u003C/p\u003E\r\n\u003Ch3\u003ESolving embedding bottlenecks to enable over 1.5 million tokens/sec GPU performance\u003C/h3\u003E\r\n\u003Cp\u003ESnowflake processes trillions of tokens per month across both real-time and batch embedding workloads. But when we benchmarked embedding models using vLLM, we uncovered performance bottlenecks — slow serialization, sequential tokenization and low GPU utilization — that left hardware severely underused.&nbsp;\u003C/p\u003E\r\n\u003Cp\u003ETo fix this, we optimized Arctic Inference with vectorized serialization, parallel tokenization and multi-instance GPU execution. As a result, it delivers 16x faster embedding inference than vLLM on short sequences and 4.2x faster on long sequences, while outperforming TEI by 2.4x on short sequences and matching it on longer ones. Learn more in our sister blog post on \u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://www.snowflake.com/en/engineering-blog/embedding-inference-arctic-16x-faster\" target=\"_blank\"\u003Eembedding throughput optimizations\u003C/a\u003E.\u003Cbr\u003E\r\n&nbsp;\u003C/p\u003E\r\n\u003Ch2\u003EBringing it all together: Proving Arctic Inference is best in class&nbsp;&nbsp;\u003C/h2\u003E\r\n\u003Cp\u003EHere, we share core results demonstrating that Arctic Inference is the fastest and most cost-effective open source inference system for enterprise AI. (For technical readers, we also include a detailed evaluation methodology in the appendix at the end of this post.)\u003C/p\u003E\r\n\u003Ch3\u003ESimultaneously, Arctic is currently the fastest and most cost-effective open-source inference system\u003C/h3\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_78181516":{"id":"image-810b369bfa","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--ae71cd8b-0a28-40ed-bdd2-855b2f2aaf51/shiftpar1.png?preferwebp=true&quality=85","alt":"Figure 1: Arctic Inference achieved highest throughput and lowest latency for Llama 3.3 70B across open source inference frameworks.","lazyEnabled":true,"height":"630","width":"1600","title":"Figure 1: Arctic Inference achieved highest throughput and lowest latency for Llama 3.3 70B across open source inference frameworks.",":type":"snowflake-site/components/image"},"blog_text_201240548":{"id":"blog-text-bbcd80bff6","text":"\u003Cp\u003EFigure 1 shows that Arctic Inference simultaneously achieves highest throughput (lowest cost) and lowest completion time — all in one deployment — outperforming the best open source systems optimized for each metric individually\u003Csup\u003E2\u003C/sup\u003E. More specifically, Arctic Inference combines Shift Parallelism with speculative decoding and SwiftKV to achieve:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E3.4x faster request completion and 1.06x higher throughput compared to SoTA throughput-optimized deployment (TP=1, DP=8)\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E1.7x higher throughput and 1.28x faster request completion compared to SoTA latency-optimized deployment (TP=8, DP=1)\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EIn Figure 1, latency-optimized and throughput-optimized configurations for vLLM, SGLang and TRT-LLM use TP=8 and DP=1 and TP=1 and DP=8, respectively, and the best speculative decoding solutions that were available for each of the frameworks (see the evaluation methodology in the appendix for details). These experiments were run on data sets generated using real-world production traces to compute throughput and a mixture of ShareGPT, HumanEval and SWEBench to measure latency. As a result, these results are representative of performance achievable in real-world deployments.&nbsp;\u003Cbr\u003E\r\n&nbsp;\u003C/p\u003E\r\n\u003Ch3\u003EAchieving the elusive trifecta: Quicker response, higher throughput and faster generation\u003C/h3\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_658148543":{"id":"image-2ea643288e","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--6b2f0dc0-3634-46c7-a148-0065d9d50f7a/shiftpar4.png?preferwebp=true&quality=85","alt":"Figure 4: Arctic Inference simultaneously archives the fastest generation, prefill and combined throughput on Llama 3.3 70B running on 8xH200. ","lazyEnabled":true,"height":"1100","width":"1522","title":"Figure 4: Arctic Inference simultaneously archives the fastest generation, prefill and combined throughput on Llama 3.3 70B running on 8xH200. ",":type":"snowflake-site/components/image"},"blog_text_2090475865":{"id":"blog-text-3df296e1d9","text":"\u003Cp\u003EResponse latency, generation speed and combined throughput are the three core pillars of inference system performance. Figure 4 shows that Arctic Inference outperforms the best open source systems optimized for each metric individually — achieving the elusive trifecta all in one deployment. More specifically, even when compared to the best deployment across vLLM, SGLang and TRT-LLM using bespoke configurations optimized for individual metrics, Arctic Inference with just a single deployment achieves:&nbsp;\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E 2.25x faster response time (prefill throughput per request)&nbsp;\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E1.75x faster generation per request\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003Eon-par combined throughput\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EThis is possible due to the symbiosis between Shift Parallelism, optimized speculative decoding implementation and SwiftKV, which all work together in Arctic Inference. The combination of Shift Parallelism and speculative decoding enables Arctic Inference to achieve the fastest generation per request. Similarly, the combination of Shift Parallelism and SwiftKV enables Arctic Inference to achieve both the highest prefill speed, resulting in the fastest response times, and the highest throughput.\u003Cbr\u003E\r\n\u003Cbr\u003E\r\nFor details on the data sets used to produce these results, see the evaluation methodology in the appendix.\u003Cbr\u003E\r\n&nbsp;\u003C/p\u003E\r\n\u003Ch3\u003E16x faster throughput when scaling vLLM embeddings\u003C/h3\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_2086181074":{"id":"image-7efe39e1f4","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--36d5c638-920a-4b8a-ae09-338cd99177eb/shiftpar5.png?preferwebp=true&quality=85","alt":"Figure 5: Arctic Inference establishes the new SoTA for embedding throughput performance, outperforming vLLM and TEI.","lazyEnabled":true,"height":"628","width":"1462","title":"Figure 5: Arctic Inference establishes the new SoTA for embedding throughput performance, outperforming vLLM and TEI.",":type":"snowflake-site/components/image"},"blog_text_1864095222":{"id":"blog-text-5dcc2e8bfe","text":"\u003Cp\u003EFigure 5 shows that Arctic Inference can process over 1.4 million tokens per second not only for long sequences but also for short ones, which are notoriously difficult to optimize.&nbsp;\u003C/p\u003E\r\n\u003Cp\u003EBy vectorizing data serialization and parallelizing tokenization, Arctic Inference helps ensure that the majority of computation time is spent on the actual embedding computation.&nbsp;\u003C/p\u003E\r\n\u003Cp\u003EAs a result, Arctic Inference can achieve:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E16x higher throughput than vLLM on short sequences and 4.2x higher throughput on long sequences\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E2.4x higher throughput than Text Embeddings Inference (TEI) on short sequences and on-parity for long sequences&nbsp;\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EFurthermore, Arctic Inference supports running multiple instances of the same embedding model on a single GPU to allow better saturation of GPU resources when using small but powerful embedding models such as the \u003Ca href=\"https://huggingface.co/collections/Snowflake/arctic-embed-661fd57d50fab5fc314e4c18\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Esnowflake-arctic-embed model family\u003C/a\u003E. You can read more about this in our blog.\u003Cbr\u003E\r\n&nbsp;\u003C/p\u003E\r\n\u003Ch3\u003EAdapting to real-world traffic without a latency-throughput trade-off\u003C/h3\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image_2060661078":{"id":"image-3723852159","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--77be85bb-56d1-486a-a3ee-f56eef9fdd4e/shiftpar6.png?preferwebp=true&quality=85","alt":"Figure 6: Shift Parallelism achieves the lowest response, fastest generation and near-optimal throughput under dynamic traffic.","lazyEnabled":true,"height":"1106","width":"1600","title":"Figure 6: Shift Parallelism achieves the lowest response, fastest generation and near-optimal throughput under dynamic traffic.",":type":"snowflake-site/components/image"},"blog_text_416337717":{"id":"blog-text-26c791c125","text":"\u003Cp\u003EFigure 6 shows that Shift Parallelism can adapt to real-world traffic, simultaneously delivering the lowest response latency (TTFT), while achieving the fastest generation (lowest TPOT), and near-optimal cost efficiency (total throughput), compared to both throughput-optimized (DP only) and latency-optimized (TP only) solutions. More specifically, Shift Parallelism achieves:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E9x reduction in median TTFT compared to the next best solution (1355ms → 148ms)\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E1.6x reduction in median TPOT compared to the next best solution (83ms → 51ms)&nbsp;\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003EMax throughput regression less than 10% compared to the best solution\u003Csup\u003E3\u003C/sup\u003E (75.5K → 69.1K toks/sec)\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EHere, Shift Parallelism dynamically shifts to using TP=8 when traffic is low, achieving the lowest TPOT, while switching to SP=8 when traffic increases, allowing for up to 1.35x higher throughput than TP=8 to avoid spikes in TTFT and TPOT.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_1040355898":{"id":"blog-text-c80d0ad39c","text":"\u003Ch2\u003EGetting started with Arctic Inference\u003C/h2\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://github.com/snowflakedb/ArcticInference/tree/main\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EArctic Inference\u003C/a\u003E is integrated with vLLM v0.8.4 using vLLM’s custom plugin feature, allowing us to develop and integrate inference optimizations quickly and make them available to the community.\u003C/p\u003E\r\n\u003Cp\u003EOnce installed, Arctic Inference automatically patches vLLM with all of the features from this blog post, and users can continue to use their familiar vLLM APIs and CLI. It’s easy to get started!\u003C/p\u003E\r\n\u003Cp\u003EInstall vLLM and Arctic Inference:\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet":{"id":"code-snippet-b6beb9be7f","codeSnippet":"pip install arctic-inference[vllm]","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_480284945":{"id":"blog-text-b8988a685c","text":"\u003Cp\u003EArctic Inference will add several additional configurations to vLLM. The example below will run Arctic Inference on eight GPUs with Shift Parallelism, LSTM draft model, suffix decoding and SwiftKV:\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_1235322817":{"id":"code-snippet-39ef7eef94","codeSnippet":"vllm serve \\\r\n    Snowflake/Llama-3.1-SwiftKV-70B-Instruct \\\r\n    --quantization \"fp8\" \\\r\n    --tensor-parallel-size 1 \\\r\n    --ulysses-sequence-parallel-size 4 \\\r\n    --enable-shift-parallel \\\r\n    --shift-parallel-threshold 512 \\\r\n    --speculative-config '{\r\n        \"method\": \"arctic\",\r\n        \"model\":\"Snowflake/Arctic-LSTM-Speculator-Llama-3.1-70B-Instruct\",\r\n        \"num_speculative_tokens\": 3,\r\n        \"enable_suffix_decoding\": true\r\n    }'","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_129265970":{"id":"blog-text-33e2097180","additionalClasses":"inline","text":"\u003Cp\u003EIn the example above, Arctic Inference will use eight-way sequence parallelism and dynamically shift to eight-way tensor parallelism when the batch size is larger than 512, specified by \u003Ccode\u003E--shift-parallel-threshold\u003C/code\u003E. In the speculation configurations, \u003Ccode\u003E&quot;method&quot;: &quot;arctic&quot;\u003C/code\u003E enables the LSTM speculator, along with the system optimizations described in this blog post. \u003Ccode\u003E&quot;enable_suffix_decoding&quot;: true\u003C/code\u003E enables suffix decoding.\u003C/p\u003E\r\n\u003Cp\u003EFor more detailed information on how to use Arctic Inference and the set of models that are supported, please check out \u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://github.com/snowflakedb/ArcticInference\" target=\"_blank\"\u003Ethis link\u003C/a\u003E.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_1084366886":{"id":"blog-text-2d8d3a27c9","text":"\u003Ch3\u003ECitation\u003C/h3\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_785570557":{"id":"code-snippet-27dae812a3","codeSnippet":"@misc{rajbhandari2025arcticinferenceshiftparallelism,\r\n      title={Arctic Inference with Shift Parallelism: Fast and Efficient Open Source Inference System for Enterprise AI}, \r\n      author={Samyam Rajbhandari and Mert Hidayetoglu and Aurick Qiao and Ye Wang and Juncheng Yang and Jeff Rasley and Michael Wyatt and Yuxiong He},\r\n      year={2025},\r\n      eprint={2507.11830},\r\n      archivePrefix={arXiv},\r\n      primaryClass={cs.DC},\r\n      url={https://arxiv.org/abs/2507.11830}, \r\n}","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_357107627":{"id":"blog-text-1f8e11d844","text":"\u003Ch2\u003EAcknowledgments\u003C/h2\u003E\r\n\u003Cp\u003EWe would like to thank Jaeseong Lee and Gabriele Oliaro for their contributions to speculative decoding optimizations in Arctic Inference.\u003Cbr\u003E\r\n\u003Cbr\u003E\r\nWe would like to thank Flex Wang, Jerry Luo, Seth Li, Ricardo Aravena, Allen Woo and Vincent Chan for their continued support in bringing our research to production and into Snowflake Cortex AI.\u003Cbr\u003E\r\n&nbsp;\u003C/p\u003E\r\n\u003Ch2\u003EAppendix\u003C/h2\u003E\r\n\u003Ch3\u003EEvaluation methodology\u003C/h3\u003E\r\n\u003Cp\u003E\u003Cb\u003EHardware: \u003C/b\u003EAll experiment results presented in this blog post, unless otherwise stated, were run on an 8xH200 GPU node, leveraging all the GPUs within the node.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EModels:\u003C/b\u003E Meta Llama 3.3 70B generative AI. Arctic Embedding model, \u003Ca href=\"https://huggingface.co/BAAI/bge-base-en-v1.5\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Ebge-base-en-v1.5\u003C/a\u003E for embedding.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ERequest completion latency and TPOT measurements: \u003C/b\u003EUnless otherwise stated, all request completion latency and TPOT measurements were done by sending one request at a time and averaged over a mixed data set consisting of ShareGPT, HumanEval and SWEBench, comprising short conversations, coding tasks and long agentic workflows. We used latency-optimized configs for all our baselines, where we used TP=8 and the best available open source speculative decoding approach supported by the baseline.&nbsp;\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003ECombined throughput measurements:\u003C/b\u003E Unless otherwise stated, for throughput measurements all requests were sent at the same time and measurements were made ensuring steady state. For Figure 1, we constructed a realistic data set with input and output lengths sampled from our Snowflake Cortex AI production logs, allowing us to create a data set representative of enterprise workloads we see at Snowflake. For all other experiments, we used input length of 2,000 and output of 250 tokens, to match the average 10:1 ratio between input and output we observe in our production logs. We used throughput-optimized configs for all our baselines, where we used TP=1 and manually tuned the batch size to achieve the highest throughput.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EPrefill throughput measurements: \u003C/b\u003EUnless otherwise stated, prefill throughput measurements were performed using a single request with a 4,000 context length. This was because we found that context length smaller than 4,000 did not saturate the GPU, while a length longer than 4,000 did not increase prefill throughput significantly.&nbsp;&nbsp;\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EOpen source baselines: \u003C/b\u003EvLLM (v0.8.4), TRT-LLM (v0.18.2), SGLang (v0.4.6)&nbsp;\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EOpen source speculative decoding baselines: \u003C/b\u003EEAGLE-based speculative decoding offers the best latency for SGLang and vLLM, but it only supports shorter than 4,000 sequences and crashes when longer sequences are sent. Hence we could not use it for our realistic data set mix, which consisted of both short and longer sequences. Due to the limitations of EAGLE, we leveraged NGRAM speculation in vLLM and no speculation in SGLang, as anything else could not support the real-world use case we see in our production. For TRT-LLM, despite our best effort, we could not get it to work with any speculative decoding in a consistent way, and therefore we reported the numbers without speculative decoding.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003EArctic Inference+vLLM: \u003C/b\u003EUnless otherwise stated, for all experiments referred to as Arctic Inference, we used a single config combining Shift Parallelism shifting between SP=8 (Arctic Ulysses) and TP=8, with \u003Ca href=\"https://www.snowflake.com/en/engineering-blog/swiftkv-llm-compute-reduction/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003ESwiftKV\u003C/a\u003E optimizations, and \u003Ca href=\"https://www.snowflake.com/en/engineering-blog/fast-speculative-decoding-vllm-arctic/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Especulative decoding optimizations\u003C/a\u003E that combine suffix decoding with our LSTM draft model. We ran Arctic Inference on top of vLLM (v0.8.4).&nbsp;\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_612505472":{"id":"blog-text-41104b0c93","text":"\u003Chr\u003E\r\n\r\n\u003Cp\u003E\u003Csup style=\"font-family: adobe-clean, &quot;Source Sans Pro&quot;, -apple-system, BlinkMacSystemFont, &quot;Segoe UI&quot;, Roboto, Ubuntu, &quot;Trebuchet MS&quot;, &quot;Lucida Grande&quot;, sans-serif;\"\u003E1\u003C/sup\u003E&nbsp;&nbsp;&nbsp;Snowflake Llama models and embedding models in Snowflake Cortex AI.\u003C/p\u003E\r\n\u003Cp\u003E\u003Csup\u003E2\u003C/sup\u003E&nbsp; &nbsp;Latency-optimized and throughput-optimized configurations for vLLM, SGLang and TRT-LLM use TP=8 and DP=1 and TP=1 and DP=8, respectively, along with the best available speculative decoding for each framework. These experiments were run on data sets generated using real-world production traces to compute throughput, and a mixture of ShareGPT, HumanEval and SWEBench to measure latency. As a result, these results are representative of performance achievable in real-world deployments. For more details, see the evaluation methodology in the appendix.\u003C/p\u003E\r\n\u003Cp\u003E\u003Csup\u003E3&nbsp;\u003C/sup\u003E&nbsp;vLLM does not allow measurements in real time, so the combined throughput as a function of time was obtained based on request start time, TTFT and generation throughput. As request arrival and first token response may not always align with the measurement time window, the calculated numbers are not always precise. However, since each parallelism config will have a similar margin of error, the relative measurements across different parallelism configurations are still very meaningful.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["image","blog_text","blog_text_2059071170","blog_text_2054978886","image_718442723","blog_text_361688486","image_1569762019","blog_text_1538335824","image_1763326756","blog_text_2130100425","image_78181516","blog_text_201240548","image_658148543","blog_text_2090475865","image_2086181074","blog_text_1864095222","image_2060661078","blog_text_416337717","blog_text_1040355898","code_snippet","blog_text_480284945","code_snippet_1235322817","blog_text_129265970","blog_text_1084366886","code_snippet_785570557","blog_text_357107627","blog_text_612505472"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium",":items":{},":itemsOrder":[],":type":"wcm/foundation/components/responsivegrid"},"container_author_chip":{"columnClassNames":{"author_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-09dba9380d",":type":"snowflake-site/components/container",":items":{"author_chip":{"id":"author-chip-ccd103136e","title":{"id":"title","type":"heading2","lines":["Learn more about the authors"],":type":"snowflake-site/components/title-v2"},"authors":[{"authorImage":{"id":"image-85fde00c17","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--746d3344-2e70-4178-b5cc-01726d6b5dc5/samyam-headshot.png?preferwebp=true&quality=85","alt":"Samyam Rajbhandari","lazyEnabled":true,"height":"326","width":"245",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-00437a0bac","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/samyam-rajbhandari/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","text":"Samyam Rajbhandari","linkType":"SNOWFLAKE_INTERNAL"},"authorTitle":"Principal AI Architect, Snowflake"},{"authorImage":{"id":"image-003d803336","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--c9728c2d-326c-4609-9f05-29a572a11fae/mert-hidayetoglu.jpg?preferwebp=true&quality=85","lazyEnabled":true,"height":"1281","width":"1281",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-4e546fbef9","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/mert-hidayetoglu/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","text":"Mert Hidayetoglu","linkType":"SNOWFLAKE_INTERNAL"},"authorTitle":"Senior Software Engineer"},{"authorImage":{"id":"image-99f1e4fc65","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--76706883-25ab-49ae-a031-c153da26f95b/aurick-qiao.jpg?preferwebp=true&quality=85","lazyEnabled":true,"height":"449","width":"449",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-212b573643","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/aurick-qiao/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","text":"Aurick Qiao","linkType":"SNOWFLAKE_INTERNAL"},"authorTitle":"Senior Software 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