X local-model bench digest — Oct 24, 2026
As of Oct 24, 2026 — 36 posts collected from X. 93 measured runs catalogued, 1 published to the board, 35 kept to the digest only. Latest source post Oct 24, 2026 10:58 UTC.
Every number below is what its author published, transcribed with the engine, quantization and context length they reported. Sources are linked on each entry. Only posts that state the full methodology (engine build, quantization, context length, hardware state, measurement method) reach the benchmark board; the rest stay here as leads.
RTX 5090 × DeepSeek-V4.1-Flash
Oct 24, 2026 · by @lyesbahi
| Context | Quant | Engine | Decode | Prefill |
|---|---|---|---|---|
| — | — | maya | 31.6 tok/s | — |
| — | — | strata 0.2.4 | 185.0 tok/s | — |
| — | — | strata 0.2.4 | 40.0 tok/s | — |
DeepSeek-V4.1-Flash on a single RTX 5090: Maya hits 31.6 tok/s decode; Strata 0.2.4 just ported the engine. Their Qwen MoE runs 185 tok/s on that card, ~40 on 2016 Tesla P40s. Maya vs Strata 🥊 one GPU, two engines - both ship faster than we can re-bench. https://t.co/66WuB6XFKF
Digest-only: confidence 0.55 below 0.60; incomplete methodology (quantization, context length, hardware state, measurement method).
RTX 5090 × DeepSeek V4.1 Flash 1017 GSQ-RCO IQ2_XS ISTA
Oct 24, 2026 · by @populartourist
- Strata 0.2.4 - 5090 + 96GB DDR5 + R9 9950X - DeepSeek V4.1 Flash 1017 GSQ-RCO IQ2_XS ISTA - 512k ctx window / 256k max tokens Decode 25<->40 tok/s Prefill ??? Still testing and tuning. https://t.co/uwdCcZYrme
Digest-only: confidence 0.55 below 0.60; incomplete methodology (measurement method).
DGX Spark x4 × DeepSeek-V4.1-Flash-1017
Oct 24, 2026 · by @majewskizby
| Context | Quant | Engine | Decode | Prefill |
|---|---|---|---|---|
| — | — | sglang dsv4.1 f80c91a4b (Dockerfile.canary-roce from f9… | 726.9 tok/s | — |
| — | — | sglang dsv4.1 f80c91a4b (Dockerfile.canary-roce from f9… | 471.6 tok/s | — |
| — | — | sglang dsv4.1 f80c91a4b (Dockerfile.canary-roce from f9… | 461.4 tok/s | — |
| — | — | sglang dsv4.1 f80c91a4b (Dockerfile.canary-roce from f9… | 359.7 tok/s | — |
| — | — | sglang dsv4.1 f80c91a4b (Dockerfile.canary-roce from f9… | 322.8 tok/s | — |
| — | — | sglang dsv4.1 f80c91a4b (Dockerfile.canary-roce from f9… | 222.2 tok/s | — |
| — | — | sglang dsv4.1 f80c91a4b (Dockerfile.canary-roce from f9… | 160.8 tok/s | — |
| — | — | sglang dsv4.1 f80c91a4b (Dockerfile.canary-roce from f9… | 142.9 tok/s | — |
| — | — | sglang dsv4.1 f80c91a4b (Dockerfile.canary-roce from f9… | 130.6 tok/s | — |
| — | — | sglang dsv4.1 f80c91a4b (Dockerfile.canary-roce from f9… | 91.3 tok/s | — |
| 128K | — | sglang dsv4.1 f80c91a4b (Dockerfile.canary-roce from f9… | — | 6291.0 tok/s |
| 4K | — | sglang dsv4.1 f80c91a4b (Dockerfile.canary-roce from f9… | — | 4798.0 tok/s |
🚨 New record: DeepSeek-V4.1-Flash on 4× DGX Spark (GB10), TP4 🏆 • 🔥 Decode: 702 tok/s* !!!!!!!11111 • 🎯 Draft acceptance: ~100 % • 🧬 Replies byte-identical to each other • 💎 Quality: 0/0 📦 Recipe: https://t.co/hptwQGS0xk *16 users, each asking it to print the digit "0" over and over 🫠
Digest-only: no run with engine+version+quant+context+decode tps; incomplete methodology (quantization).
RTX 5090 × DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF
Oct 24, 2026 · by @jun1228909
| Context | Quant | Engine | Decode | Prefill |
|---|---|---|---|---|
| 128K | IQ2_XXS | strata | 14.0 tok/s | — |
DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF IQ2_XXS on Strata お試し i7-14700/5090/DDR5 128GB/Windows11/dsh/128K ctx 推論エフォート Off 「マイクラ風3D箱庭ゲーム作って」 2h 01m 07s で完走 常時 14 tok/s 前後 動いた！！🚀🚀 今回のマイクラに関しては、Q2, エフォート Off なので流石にクオリティはちょっとねｗ ただ、みんな思い出して欲しい 最近の Strata マジックで感覚が麻痺しているかもしれないが 763B のモデルだからな😂 それが 5090 単騎で普通に動かせていることがすごい🙌 他ユーザーの報告によると、V100 や 4060ti などでも動き始めてるね DS 1017 に関しては、先行していた Maya 関連の報告が蓄積されているので 今後は一つ、比較対象になっていくかもしれないな👀…
Digest-only: no run with engine+version+quant+context+decode tps; incomplete methodology (engine+version, hardware state, measurement method).
RTX 5090 × DeepSeek-V4.1-Flash
Oct 24, 2026 · by @DaSun64125381
| Context | Quant | Engine | Decode | Prefill |
|---|---|---|---|---|
| — | — | — | 27.0 tok/s | 225.0 tok/s |
DeepSeek-V4.1-Flash, running at home on my RTX 5090. ~27 tok/s decode, ~225 tok/s prefill, 274 GB of local weights. Speed still needs work, but this is very promising. Looking forward to more.@coldniko https://muse.ai/s/report-xoj6qgwxvxx1x0xw/DhOHMluXi9egfHi0IIc-9HlLh4oNsLGwtxQswWceuPU https://t.co/plexfguxDb
Digest-only: confidence 0.00 below 0.60; incomplete methodology (engine+version, quantization, context length, hardware state, measurement method).
DGX Spark x2 × MiniMax M3.1 Flash
Oct 24, 2026 · by @Tech2Wild
| Context | Quant | Engine | Decode | Prefill |
|---|---|---|---|---|
| 300K | EXL3 SAGE 2.15 bpw | vllm 41fdb7d + tp2-minimax-indexer.patch + tp2-minima… | 39.4 tok/s | — |
| 300K | EXL3 SAGE 2.15 bpw | vllm 41fdb7d + tp2-minimax-indexer.patch + tp2-minima… | 30.4 tok/s | — |
| 300K | EXL3 SAGE 2.15 bpw | vllm 41fdb7d + tp2-minimax-indexer.patch + tp2-minima… | 19.8 tok/s | — |
| 300K | EXL3 SAGE 2.15 bpw | vllm 41fdb7d + tp2-minimax-indexer.patch + tp2-minima… | 78.1 tok/s | — |
| 300K | EXL3 SAGE 2.15 bpw | vllm 41fdb7d + tp2-minimax-indexer.patch + tp2-minima… | 66.2 tok/s | — |
| 300K | EXL3 SAGE 2.15 bpw | vllm 41fdb7d + tp2-minimax-indexer.patch + tp2-minima… | 47.6 tok/s | — |
| 300K | EXL3 SAGE 2.15 bpw | vllm 41fdb7d + tp2-minimax-indexer.patch + tp2-minima… | 46.5 tok/s | — |
| 5350 | EXL3 SAGE 2.15 bpw | vllm 41fdb7d + tp2-minimax-indexer.patch + tp2-minima… | — | 566.0 tok/s |
| 97510 | EXL3 SAGE 2.15 bpw | vllm 41fdb7d + tp2-minimax-indexer.patch + tp2-minima… | — | 527.0 tok/s |
| 128206 | EXL3 SAGE 2.15 bpw | vllm 41fdb7d + tp2-minimax-indexer.patch + tp2-minima… | — | 501.0 tok/s |
🔥 MiniMax M3.1 Flash (428B) on 2 NVIDIA DGX Sparks, TP2 over RoCE 🧠 Quant by @ViC305 (SAGE EXL3 2.15 bpw) 🙌 📏 300K context, 726K KV pool 🏎️ Up to 39 tok/s single stream, 65-78 tok/s at 4 users 🛠️ 2 vLLM-EXL3 patches for TP2 Recipe + benchmarks 👇 https://github.com/tonyd2wild/MiniMax-M3.1-Flash-EXL3-TP2-2x-DGX-Spark
Full methodology stated (engine, quantization, context, hardware state, method) — 1 added to the benchmark board (1 of 10 measured rows queued; the rest are published here only).
Unspecified platform × Qwen 3.8 27b
Oct 24, 2026 · by @_imdawon
| Context | Quant | Engine | Decode | Prefill |
|---|---|---|---|---|
| — | — | — | 80.0 tok/s | — |
shipping tomorrow! few more things to button up for coding harnesses. Qwen 3.8 27b unlimited tokens @ 80 tok/s
Digest-only: confidence 0.40 below 0.60; hardware.name missing; incomplete methodology (engine+version, quantization, context length, hardware state, measurement method).
RTX 3060 × DeepSeek-V4.1-Flash-1017
Oct 24, 2026 · by @NFT_Chen · third-party report
| Context | Quant | Engine | Decode | Prefill |
|---|---|---|---|---|
| — | IQ2_XXS (2.00 bpw, ~249GB) | strata | 9.0 tok/s | — |
🔥牛逼了！12GB显卡本地跑763B！DeepSeek-V4.1-Flash-1017量化版炸裂登场，前沿质量几乎零损失！ ISTA-DASLab团队发布GSQ-RCO非均匀量化版，Strata引擎原生支持。763B参数MoE大模型最低压到1.75 bpw，在消费级硬件上就能跑！ 核心亮点 🔹极致压缩 + 几乎无损：IQ2_XXS（2.00 bpw，约249GB）在AIME25、GPQA-Diamond、LiveCodeBench v6上100%恢复原始模型分数；IQ1_M（1.75 bpw，约231GB）整体恢复98.7%。 🔹真正本地可行：单张12GB显卡 + 46-64GB系统内存即可运行，Engram表（约111GB）直接SSD内存映射，无需全部装进内存/显存。 🔹推理速度可玩：RTX 3060上IQ2_XXS约8-10 tok/s；更高端卡更快。原生OpenAI兼容API，支持推理effort调节。…
Digest-only: confidence 0.55 below 0.60; incomplete methodology (engine+version, context length, hardware state, measurement method).
RX 580 8GB + RX 570 8GB + GTX 1660 Super 6GB + GTX 1070 8GB × Qwen3.8:27b-vision, Qwen3.5:9b-vision, Qwen3.5:2b-vision
Oct 24, 2026 · by @jurlycat · third-party report
| Context | Quant | Engine | Decode | Prefill |
|---|---|---|---|---|
| — | — | slipstream (vulkan) | 3.8 tok/s | — |
| — | — | slipstream (vulkan) | 4.8 tok/s | — |
| — | — | slipstream (vulkan) | 12.5 tok/s | — |
u/FieldMouse-AI is running three Qwen vision models simultaneously on this rig. 💀 An i7-6700K paired with: • RX 580 8GB — PCIe x16 • RX 570 8GB — PCIe x1 • GTX 1660 Super 6GB — PCIe x2 • GTX 1070 8GB — PCIe x1 Slipstream orchestrates the setup using a Vulkan backend that supports both AMD and NVIDIA GPUs. Reported speeds with all three running: • Qwen3.8:27b-vision → 3.82 tok/s • Qwen3.5:9b-vision → 4.75 tok/s •…
Digest-only: no run with engine+version+quant+context+decode tps; incomplete methodology (engine+version, quantization, context length, measurement method).
Unspecified platform × DeepSeek V4.1 Flash
Oct 24, 2026 · by @Smlay_ero · third-party report
| Context | Quant | Engine | Decode | Prefill |
|---|---|---|---|---|
| — | — | — | 200.0 tok/s | — |
Kimi K3.1 Pro sits on the edge of the top tier, API list price $2/$10—cheaper than last-gen K3—but that first top-up tier is brutal: $1 gets you 3 RPM and 1.5M tokens/day. Want it to run smooth? Keep feeding it money. Highspeed at $4/$20 trades speed for quota, even worse for casual testing. Claude / GPT flagships? Strong as hell, also absurdly expensive—daily volume basically nopes out. DeepSeek V4.1 Flash…
Digest-only: confidence 0.35 below 0.60; hardware.name missing; incomplete methodology (engine+version, quantization, context length, hardware state, measurement method).
Tesla V100 32GB x3 × DeepSeek-v4.1-Flash-1017
Oct 23, 2026 · by @ItsmeAjayKV
| Context | Quant | Engine | Decode | Prefill |
|---|---|---|---|---|
| — | — | maya | — | 420.0 tok/s |
| — | — | maya | — | 1400.0 tok/s |
Heads up! Maya just got faster ! On same 3x Tesla V100 32GB + DDR4 system running same DeepSeek-v4.1-Flash-1017 quant just got a whole lot faster. Prefill is improved by almost 3x, before: ~420 tok/s after: ~1400 tok/s That's huge, all while decode is not going down !! @PeasantSmith is moving fast with Maya, much appreciated.
Digest-only: no run with engine+version+quant+context+decode tps; incomplete methodology (engine+version, quantization, context length, hardware state, measurement method).
NVIDIA B300 × Muse Spark Open
Oct 23, 2026 · by @ArtificialAnlys
Results from Muse Spark Open, Meta’s newly released 816B (42B active) open-weights model, are now live on AA-AgentPerf. In our initial testing, the model reaches up to ~150M tokens/s throughput per MW of power used on 4x NVIDIA GB300 or B300 GPUs Muse Spark Open scores 39 on the Artificial Analysis Intelligence Index. It is an MoE with 62 layers, of which 46 use 2,048-token sliding-window attention, cutting the KV…
Digest-only: no run with engine+version+quant+context+decode tps; incomplete methodology (engine+version, context length, hardware state, measurement method).
Also in this window
The 12 most recent posts are shown in full above; 24 posts from the same window are listed here in short form, each with its source post.
- Unspecified platform × DeepSeek-v4.1-Flash-1017 — 212.0 tok/s decode (2 measurements) · @nothingto12
- Unspecified platform — 60.0 tok/s decode (1 measurement) · @silennai
- Unspecified platform — 60.0 tok/s decode (1 measurement) · @painn_x
- DGX Spark — 102.1 tok/s decode (1 measurement) · @petruspennanen
- DGX Spark × qwen3.8-27b — 49.9 tok/s decode (2 measurements) · @0xBakeer
- Snapdragon 8 Elite Gen 5 × Bonsai 2 2B — 40.0 tok/s decode (1 measurement) · @mav3ri3k
- DGX Spark x2 × DeepSeek V4.1-Flash-1017 — 153.0 tok/s decode (9 measurements) · @bertholomusai
- RTX 5090 × DeepSeek-V4.1-Flash-1017 — 22.9 tok/s decode (3 measurements) · @niklaslenz_ai
- RTX 3090 x7 × GLM 5.3 Flash — 2980.0 tok/s prefill (3 measurements) · @VRAMCalculator
- RTX 5090 × DeepSeek-V4.1-Flash-1017 — 22.9 tok/s decode (6 measurements) · @niklaslenz_ai
- Unspecified platform × deepseek-v4.1-flash-1017 — 735.0 tok/s decode (1 measurement) · @0xSero
- Unspecified platform × deepseek-v4.1-flash-1017 — 650.0 tok/s decode (1 measurement) · @0xSero
- RTX 4070 Laptop × Qwen3.6-35B-A3B — 76.6 tok/s decode (7 measurements) · @ArnavPrabhu
- DGX Spark x2 × deepseek-v4.1-flash-1017 — 102.8 tok/s decode (4 measurements) · @atasrb
- GB10 x4 × DeepSeek V4.1-Flash — 2949.0 tok/s prefill (1 measurement) · @bertholomusai
- Unspecified platform — 120.0 tok/s decode (1 measurement) · @laopai1990
- DGX Spark x2 × DeepSeek-V4.1-Flash-1017 — 66.3 tok/s decode (5 measurements) · @niklaslenz_ai
- Unspecified platform × Kimi k3.1 — 120.0 tok/s decode (1 measurement) · @ai_funss
- RTX 5090 × 125B model (unspecified) — 177.0 tok/s decode (3 measurements) · @seanhighness
- Unspecified platform — 117.0 tok/s decode (1 measurement) · @laopai1990
- RTX 4090 — 50.0 tok/s decode (1 measurement) · @JohnCLangford
- Intel Arc Pro B60 × Qwen3.8-27B — 17.3 tok/s decode (1 measurement) · @QwenAIFans
- Unspecified platform — 1500.0 tok/s decode (1 measurement) · @RayaneRachid_
- RTX 5090 x6 × 284B model (unnamed) — 30.1 tok/s decode (1 measurement) · @ridark_eth
How to read this page
This digest is an automated transcription of public posts. Runs with a stated engine build, quantization, context length, hardware state and measurement method are published straight to the benchmark board with a link to their source; the rest stay here. These are author-reported numbers, not an independent DiggTop re-measurement: every row carries its provenance so you can check the original, and a number that turns out to be misreported is corrected or removed. Our own testing follows the methodology. Spotted an error? Reply on the source post or submit a corrected run at /submit.
Sources
- @lyesbahi — Oct 24, 2026
- @populartourist — Oct 24, 2026
- @majewskizby — Oct 24, 2026
- @jun1228909 — Oct 24, 2026
- @DaSun64125381 — Oct 24, 2026
- @Tech2Wild — Oct 24, 2026
- @_imdawon — Oct 24, 2026
- @NFT_Chen — Oct 24, 2026
- @jurlycat — Oct 24, 2026
- @Smlay_ero — Oct 24, 2026
- @ItsmeAjayKV — Oct 23, 2026
- @ArtificialAnlys — Oct 23, 2026
- @nothingto12 — Oct 23, 2026
- @silennai — Oct 23, 2026
- @painn_x — Oct 23, 2026
- @petruspennanen — Oct 23, 2026
- @0xBakeer — Oct 23, 2026
- @mav3ri3k — Oct 23, 2026
- @bertholomusai — Oct 23, 2026
- @niklaslenz_ai — Oct 23, 2026
- @VRAMCalculator — Oct 23, 2026
- @niklaslenz_ai — Oct 23, 2026
- @0xSero — Oct 23, 2026
- @0xSero — Oct 23, 2026
- @ArnavPrabhu — Oct 23, 2026
- @atasrb — Oct 23, 2026
- @bertholomusai — Oct 23, 2026
- @laopai1990 — Oct 23, 2026
- @niklaslenz_ai — Oct 23, 2026
- @ai_funss — Oct 23, 2026
- @seanhighness — Oct 23, 2026
- @laopai1990 — Oct 23, 2026
- @JohnCLangford — Oct 23, 2026
- @QwenAIFans — Oct 23, 2026
- @RayaneRachid_ — Oct 23, 2026
- @ridark_eth — Oct 23, 2026
FAQ
Are the numbers in this digest sourced?
They are transcribed from public X posts and the repositories those posts link to, with attribution and a source link on every entry. Rows that state the full methodology are published to the benchmark board automatically, so treat them as author-reported until a dispute is raised: the source link on every row is the check.
What does it take for a row to reach the sourced tier?
Engine and exact version, quantization, context length, hardware state and a described measurement method. Posts that state all five are published to the benchmark board as soon as the collector reads them, each row carrying its source link — nothing waits in a human review queue. Anything missing a condition stays in this digest.
Where does the data come from?
Public X posts about local-model inference measured on owned hardware, discovered by keyword search every two hours and linked back to the original thread. The digest is regenerated daily.