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.

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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 →

Model: GLM-4.7
Compare setup details

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

GLM-4.7 Flash / b8118 ROCm / PP512 TG128 at prepared depth8000

AMD Radeon 8060S gfx1151 (Strix Halo; CPU SKU and installed RAM unknown) · llama.cpp

generation:
23.7 tokens/s
prompt processing (prefill):
192.8 tokens/s
quant:
UD-Q8_K_XL (GGUF)
kv:
Q8
flash attention:
on
mtp (multi-token prediction):
off

User reports GLM-4.7 at 23.7 t/s generation and 192.8 t/s prompt processing on a single Radeon 8060S gfx1151 device. Setup is llama.cpp build b8118 with UD-Q8_K_XL, q8_0 KV cache, -ngl 999, -fa 1, -ub 2048 -b 2048, and --mmap 0 -dio 1 on the ROCm/HIP arm. The headline pair comes from the same ROCm/HIP run, with exact rates 23.66 ± 1.80 t/s TG128 and 192.79 ± 3.02 PP512 at depth 8000; the Vulkan arm measured 21.14 ± 1.39 and 163.58 ± 1.83.

Oct 13, 2026

GLM-4.7 Flash / b8118 ROCm / PP512 TG128 at prepared depth8000

AMD Radeon 8060S gfx1151 (Strix Halo; CPU SKU and installed RAM unknown) · llama.cpp

generation:
26.5 tokens/s
prompt processing (prefill):
234.0 tokens/s
quant:
UD-Q6_K_XL (GGUF)
kv:
Q8
flash attention:
on
mtp (multi-token prediction):
off

User reports GLM-4.7 at 26.5 t/s generation and 234 t/s prompt processing on a single Radeon 8060S gfx1151 device. Setup is llama.cpp b8118 with UD-Q6_K_XL, q8_0 KV cache, -ngl 999, -fa 1, -ub 2048 -b 2048, and ROCm/HIP with BLAS. The headline pair comes from the same ROCm arm, measured as PP512 233.98 ± 2.41 and TG128 26.49 ± 2.30 tok/s at depth 8000, with no cross-pairing.

Oct 13, 2026

GLM-4.7 Flash / b8118 ROCm / PP512 TG128 at prepared depth8000

AMD Radeon 8060S gfx1151 (Strix Halo; CPU SKU and installed RAM unknown) · llama.cpp

generation:
36.0 tokens/s
prompt processing (prefill):
267.3 tokens/s
quant:
UD-Q4_K_XL (GGUF)
kv:
Q8
flash attention:
on
mtp (multi-token prediction):
off

User reports GLM-4.7 at 36 t/s generation and 267.3 t/s prompt processing on a single Radeon 8060S gfx1151 device. Setup is llama.cpp b8118 with UD-Q4_K_XL and q8_0 KV cache, 16 CPU threads, -ngl 999, -fa 1, -ub 2048 -b 2048, no speculative draft. The headline figures are the ROCm/HIP arm, measured as PP512 267.25 ± 1.56 and TG128 35.95 ± 3.53 tok/s at depth 8000; the 8000-token prefix is excluded from timing.

Oct 13, 2026
Apple hardware
generation:
52.6 tokens/s
prompt processing (prefill):
413.7 tokens/s
quant:
4-bit (source config) (MLX)

User reports GLM-4.7 at 52.6 t/s generation and 413.7 t/s prompt processing on a single Apple M2 Max with 64 GB unified memory. Setup is MLX with a 4-bit artifact, batch size 1, requested 512-token prompt and 128-token generation; KV cache precision and prefill chunk size are unreported. The figures are three-trial means after discarding Trial 1, with sample SD 0.04 for TG and 0.38 for PP; peak memory was 17.497 in engine output units.

Oct 13, 2026
Tone: positiveNVIDIA hardware
generation:
43.2 tokens/s
codingtool-useagentic

User reports glm-4.7-flash at 43.16 tok/s on a Framework host with Core Ultra CPU and RTX 3060 12GB. Setup is Ollama with a 192-token generation cap; the model scored 5/5 on the repo's compact coding, tool-use and agent-orchestration quality suite. It is the fastest perfect-quality model in the capture, ahead of qwen3.5:35b-a3b at 39.90 tok/s and qwen3-coder-next at 32.55 tok/s. A separate llama-server comparison on a T5500 host lists omnicoder:9b-q4_k_m at 1.48 tok/s, qwen3.5:35b-a3b-q2_k_l at 3.28 tok/s, lfm2:24b at 4.36 tok/s and granite4:7b-a1b-h at 6.48 tok/s.

Oct 7, 2026
Tone: positiveNVIDIA hardware
generation:
168.0 tokens/s

User reports GLM-4.7-Flash at 168 tok/s on a single NVIDIA H100 80GB, up from a 120 tok/s baseline with EAGLE3 speculative decoding. Setup is SGLang v0.5.6 with the FlashInfer backend, a 277 MB EAGLE3 draft head, 6 draft tokens proposed per step, and a 40% acceptance rate averaging 2.4 accepted tokens per verification step. At batch size 32 the same setup reaches 440 tok/s versus 259 tok/s baseline (1.70x), with per-request latency improving 1.30x. The user also reproduced the EAGLE3 paper's Llama-3.1-8B result at 1.25x versus the published 1.32x, and notes a SGLang token-counting bug that deflated measured throughput by about 35%.

Oct 7, 2026

GLM-4.7 30B (3B active) Flash

AMD Radeon 680M · llama.cpp

Tone: mixed
generation:
13.1 tokens/s
prompt processing (prefill):
160.3 tokens/s
quant:
Q4_K_XL (GGUF)
codingrpcreative-writingtool-usesummarizationvisionagenticmathmultilinguallong-context

User benchmarks GLM-4.7 Flash (30B-A3B MoE) on an AMD Radeon 680M iGPU with 64GB DDR5 RAM, comparing MXFP4, Q4_K_M, and Q4_K_XL quants. The Q4_K_XL quant achieves the fastest generation at 13.13 t/s, while MXFP4 leads prompt processing at 258.36 t/s. Setup uses llama.cpp with Vulkan backend and Flash Attention enabled; models are offloaded to system RAM due to iGPU memory limits.

Oct 5, 2026

GLM-4.7 30B Flash

Radeon 680M · llama.cpp

generation:
11.7 tokens/s
prompt processing (prefill):
258.4 tokens/s
quant:
MXFP4 (GGUF)

User benchmarks GLM-4.7 Flash quants on a Radeon 680M iGPU. MXFP4 gives the fastest prompt processing at 258.36 t/s and Q4_K_XL the fastest generation at 13.13 t/s.

Sep 18, 2026
Tone: positiveAMD hardware
generation:
40.0 tokens/s

User reports running 3 local sessions of GLM 4.7 at about 40 tok/s each on an AMD Strix Halo mini PC with 128 GB unified RAM. The model family is inferred as GLM-4.7 from the title and text. The user is positive about the setup for budget local AI.

Sep 9, 2026
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Community benchmarks snapshot

Records by GPU

AMD Strix Halo 128GB313NVIDIA RTX 3090217NVIDIA RTX 5090161NVIDIA DGX Spark87NVIDIA RTX Pro 6000 Blackwell70NVIDIA RTX 5060 Ti 16GB67M5 Max 48GB67NVIDIA RTX 3060 12GB63AMD Radeon AI PRO R9700 32GB61NVIDIA RTX 409055

Records by model

2584 total
Qwen3.81215
Qwen3.6274
Gemma 4165
DeepSeek V4 Flash148
Qwen3.5116
Qwen371
other595

Records by engine

1908 total
llama.cpp983
vLLM201
Strata132
MLX106
Ollama79
other407

Use cases

coding 685agentic 434long-context 323tool-use 189vision 126summarization 61math 51creative-writing 43multilingual 26rp 12text-generation 9reasoning 3
coding685agentic434long-context323tool-use189vision126summarization61math51creative-writing43

Median generation t/s by GPU

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

RTX 509073RTX Pro 600067M5 Ultra 256GB50RX 7900 XTX43RTX 409040RTX 309038RX 7800 XT 16GB35RTX 5090 Laptop 24GB34M3 Ultra 512GB32V100 32GB31

Prompt processing, same model

M5 Ultra 256GB1800RX 7900 XTX640RTX 40901949RTX 3090561M3 Ultra 512GB391V100 32GB941Radeon AI PRO R9700 32GB1287RTX 3080 20GB997Arc Pro B70512M1 Ultra 128GB153

Reports by model size

Qwen3.8 27B682Qwen3.8 125B · 6B active472Qwen3.6 35B · 3B active184DeepSeek V4 Flash 284B · 13B active131Qwen3.6 27B79Gemma 4 26B · 4B active59GLM-5.3 320B · 18B active42DeepSeek V4.1 Flash 552B · 16B active42

Quants

Q4_K_M332UD-Q4_K_XL153NVFP4144Q8_01054-bit87IQ4_XS75Q4_K_XL74MXFP465IQ3_XXS61Q454