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.

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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 RTX 5070 Ti
Compare setup details (1 active)

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

Tone: mixedNVIDIA hardware
generation:
147.0 tokens/s
quant:
IQ2_XS (GGUF)
kv:
int8

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-useagenticsummarizationlong-context

User reports Qwen3.8 Flash-Next IQ2_XS at 147 tok/s decode on an RTX 5070 Ti 16 GB with 64 GB DDR5-4800, after running Strata's --calibrate. Setup is Strata v0.1.43 with IQ2_XS GGUF, int8 KV cache, 131k context, --kv-resident 32768, MTP --spec 4, reasoning_budget_tokens 8192, conversation cache 4 GiB, vision encoder on CPU. Calibration raised median decode from 118 to 143-147 tok/s and cut the full 28-task x2 run from 20.6 to 15.4-16.0 min, but the benchmark score dropped from 50 to 44/56 twice, all in soft exact-match cases. Greedy decoding scored 50/56 versus 37 with Qwen's recommended sampling (temp 1.0/top_p 0.95/top_k 20), 43 at temp 0.6 and 40 at temp 1.0 + min_p 0.05. reasoning_effort high scored 42/56 and was twice as slow. Run-to-run noise is about +/-3/56. Real agent workload (Hermes, 54k context) gave about 100-104 tok/s decode with 78-83% expert-cache hit rate before calibration. RAM runs at 4800 MT/s with EXPO off, which the user estimates costs a few to ~20% versus DDR5-6000.

Oct 16, 2026
Tone: mixedNVIDIA hardware
generation:
81.9 tokens/s
quant:
IQ3_S (GGUF)
kv:
int8

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.

agentictool-uselong-contextsummarizationcoding

User reports Qwen3.8-Flash-Next IQ3_S at 81.9 tok/s in calibration and 85.4 tok/s in chat on an RTX 5070 Ti 16GB with 64GB RAM. Setup is Strata with IQ3_S GGUF, int8 KV cache, 256K context, KV streaming with 32768 resident cells, and MTP speculation. The model is 125B-A6B with 46.84 GiB of experts loaded and an 84GB download. Two concurrent 256K slots exceeded a 58 GiB container limit during startup with and without batch MTP, and earlier unrestricted runs caused global OOMs and a host freeze. The user settled on one active request at 256K with KV streaming.

Oct 11, 2026
Tone: positiveNVIDIA hardware
generation:
53.5 tokens/s
quant:
IQ3_S (GGUF)

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.

agenticcodinglong-context

User reports Qwen3.8-Flash-Next at 53.5 tok/s with a cached prefix on an RTX 5070 Ti 16 GB, up from 17.2 tok/s before tuning. Setup is Strata with IQ3_S at 262K context, KV cache in RAM streaming to a 32K VRAM window, speculative decoding with depth 6 and min-p 0.70, and 13 pool workers on a 14700KF with 96 GB RAM. Cold run was 43 tok/s; the 53.5 figure is with the prefix cached, which the user says is what agent sessions actually do. The user also reports the calibrator's own curve at 42.1 tok/s at --pcie-frac 0.0 down to 20.7 at 0.75.

Oct 4, 2026
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Community benchmarks snapshot

Records by GPU

AMD Strix Halo 128GB303NVIDIA RTX 3090195NVIDIA RTX 5090140NVIDIA DGX Spark80M5 Max 48GB66NVIDIA RTX Pro 6000 Blackwell64NVIDIA RTX 5060 Ti 16GB61NVIDIA RTX 3060 12GB57AMD Radeon AI PRO R9700 32GB56M4 Max 128GB49

Records by model

2326 total
Qwen3.81081
Qwen3.6244
Gemma 4154
DeepSeek V4 Flash148
Qwen3.5102
Qwen370
other527

Records by engine

1730 total
llama.cpp910
vLLM189
MLX102
Strata93
Ollama68
other368

Use cases

coding 593agentic 378long-context 291tool-use 164vision 114summarization 55math 46creative-writing 39multilingual 25text-generation 9rp 9reasoning 3
coding593agentic378long-context291tool-use164vision114summarization55math46creative-writing39

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 309036RX 7800 XT 16GB35RTX 5090 Laptop 24GB34RTX 409032V100 32GB31Radeon AI PRO R9700 32GB30

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 27B586Qwen3.8 125B · 6B active411Qwen3.6 35B · 3B active135DeepSeek V4 Flash 284B · 13B active131Qwen3.6 27B72Gemma 4 26B · 4B active37GLM-5.3 320B · 18B active36DeepSeek V4.1 Flash 552B · 16B active29

Quants

Q4_K_M304UD-Q4_K_XL145NVFP4126Q8_0994-bit83IQ4_XS71Q4_K_XL71MXFP461IQ3_XXS58Q452