llamaperf

Local model performance on your hardware

Find which open-weight LLMs fit in your GPU or Mac and compare the speeds people report on setups like yours.

What runs on your hardware?

Pick your GPU or Mac, then read the speeds people reported on it, or estimate which models fit and how fast they run.

Free to use. No account needed. Memory estimates and community measurements are labelled separately.

Looking for a particular model?

Search for a model to see its reported speeds across GPUs and Macs.

Local model performance reports from the community

These are individual setups, not a controlled benchmark. Compare GPU count, quantization, context and offloading before comparing speeds. How to read a report →

GPU: NVIDIA DGX Spark
Compare setup details (1 active)

Exact recorded values. Context may be a configured limit; matching filters does not establish identical prompts, offloading or concurrency.

NVIDIA hardware
generation:
87.7 tokens/s
quant:
MXFP4 (safetensors)
kv:
FP8

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is shown only when the source reports it; generation speed cannot tell us time to first token. Check the full setup before comparing.

codingtool-usevisionlong-context

User reports DeepSeek-V4.1-Flash at 87.7 tok/s single-stream decode on four NVIDIA DGX Sparks (TP=4). Setup is SGLang with native MXFP4 experts and FP8 dense weights, DSpark speculative decoding (k=5), 1M context configured, 8M-token KV pool pinned, ~77 GiB resident weights per rank. Aggregate decode reaches 342.7 tok/s at 16 streams (22.2 per stream); cold prefill is 4,059-5,925 tok/s at 4k-262k context, 4.9k-5.0k tok/s on real text at 16k-128k. A 1,011,084-token needle test passed. The 3-Spark TP=3 profile is also documented at 37.9 tok/s single-stream.

Oct 8, 2026

Qwen3.8 27B

NVIDIA DGX Spark · SGLang · 262,144 ctx

Tone: positiveNVIDIA hardware
generation:
41.0 tokens/s
quant:
NVFP4 (NVFP4)
kv:
fp8_e4m3

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is shown only when the source reports it; generation speed cannot tell us time to first token. Check the full setup before comparing.

codingmathagentic

User benchmarks Qwen3.8-27B in NVFP4 on a single NVIDIA DGX Spark (GB10, 128 GB unified memory), reporting a greedy median of 41.0 tok/s with SGLang + DSpark speculative decoding. Setup is SGLang with NVFP4 weights, fp8_e4m3 KV cache, 262144 max context, and DSpark block-drafter speculation; vLLM + MTP is compared on the same harness. Per-workload SGLang figures range from 37.4-41.8 tok/s on code, 40.1-44.5 on reasoning, 40.3-52.9 on math, and 18.0-19.6 on free prose. vLLM single-stream reaches 20.0 tok/s on random 512/512 and 18.0 on ShareGPT; both engines converge to roughly 60 tok/s aggregate at concurrency 4. SGLang serves 4x the context (262K vs 65K) and has lower TTFT and TPOT.

Oct 3, 2026
NVIDIA hardware
generation:
34.0 tokens/s
quant:
NVFP4 (NVFP4)

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is shown only when the source reports it; generation speed cannot tell us time to first token. Check the full setup before comparing.

User reports Qwen3.8-27B at about 34 tok/s real-world on a single DGX Spark (ASUS Ascent GX10, GB10, 128GB unified memory). Setup is SGLang with an NVFP4 W4A4 checkpoint and DSpark block-speculative decoding, batch-1 single-stream. User also measured 38.0 tok/s average on eval-style workloads and 46.7 tok/s peak on GSM8K-style prompts. On the same machine, llama.cpp UD-Q4_K_XL with MTP reached about 27 tok/s real-world and 24-30 tok/s on eval-style workloads, while vLLM 0.27 NVFP4 with MTP reached about 24.5 tok/s.

Sep 30, 2026
Tone: positiveNVIDIA hardware
generation:
35.0 tokens/s
quant:
NVFP4

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is shown only when the source reports it; generation speed cannot tell us time to first token. Check the full setup before comparing.

codingagentic

User reports Qwen3.8 Flash-Next at about 35 t/s on a single DGX Spark. Setup is SGLang with an NVFP4 quant at 262,144 context, used through VSCode Copilot in autopilot mode. The session produced about 10k lines of code and consumed about 800k tokens over 8 hours of planning, coding, and testing.

Sep 18, 2026
NVIDIA hardware
generation:
51.1 tokens/s
quant:
NVFP4

Reported by the source; GPU count, offloading and concurrent requests can change these figures. Prompt-processing speed is shown only when the source reports it; generation speed cannot tell us time to first token. Check the full setup before comparing.

User reports 51.06 t/s single-stream and 184.03 t/s aggregate at c6 on 2x DGX Spark in TP2. Setup is SGLang with QSA, NEXTN and FlashInfer GDN. Disabling NEXTN gives 26.4 t/s at c1 and 111.8 t/s at c6. Medium thinking scored 22/24 on a 24-task LiveBench-style pilot.

Sep 7, 2026
Showing 1–5 of 5
Page 1 of 1

Community benchmarks snapshot

Records by GPU

AMD Strix Halo 128GB294NVIDIA RTX 3090190NVIDIA RTX 5090134NVIDIA DGX Spark77M5 Max 48GB65NVIDIA RTX Pro 6000 Blackwell62NVIDIA RTX 5060 Ti 16GB58NVIDIA RTX 3060 12GB54AMD Radeon AI PRO R9700 32GB50M4 Max 128GB49

Records by model

2211 total
Qwen3.81021
Qwen3.6238
DeepSeek V4 Flash147
Gemma 4144
Qwen3.597
Qwen353
other511

Records by engine

1645 total
llama.cpp875
vLLM189
MLX100
Strata70
Ollama62
other349

Use cases

coding 556agentic 357long-context 272tool-use 153vision 109summarization 53math 43creative-writing 37multilingual 23text-generation 9rp 8reasoning 3
coding556agentic357long-context272tool-use153vision109summarization53math43creative-writing37

Median generation t/s by GPU

On Qwen3.8 27B at 4-bit, plain single-GPU runs. Full ranking

RTX 509082RTX Pro 600067M5 Ultra 256GB50RX 7900 XTX48RTX 309036RTX 5090 Laptop 24GB34RTX 409032V100 32GB31Radeon AI PRO R9700 32GB30RX 7800 XT 16GB30

Prompt processing, same model

M5 Ultra 256GB1800RX 7900 XTX530RTX 3090561RTX 40901949V100 32GB941RTX 3080 20GB997Arc Pro B70512M1 Ultra 128GB153Arc Pro B60 24GB380M1 Max 32GB82

Reports by model size

Qwen3.8 27B566Qwen3.8 125B · 6B active371DeepSeek V4 Flash 284B · 13B active130Qwen3.6 35B · 3B active129Qwen3.6 27B72Gemma 4 26B · 4B active36GLM-5.3 320B · 18B active33DeepSeek V4.1 Flash 552B · 16B active28

Quants

Q4_K_M290UD-Q4_K_XL140NVFP4120Q8_0984-bit79Q4_K_XL71IQ4_XS70MXFP458IQ3_XXS52Q450