Can I run it?
Can I run GLM 4.7 Flash on GeForce RTX 3080 10GB?
Expert offload
Expert offload on your GeForce RTX 3080 10GB: Q4_K_M keeps shared weights on the GPU and about 9.81 GB of experts in system RAM. About 53–72 tok/s. Active-parameter speed is approximate.
Best quantQ4_K_M
Working context8K
Max that fits8K
Speed53–72 tok/sEstimate from memory bandwidth, not a benchmark. Method.
Weights17.1 GB
KV cache0.41 GB
Total8.13 GB
Usable9.03 GB
GLM 4.7 Flash details · GeForce RTX 3080 10GB page · How this estimate is made
Run commands
llama.cpp
# Expert offload is approximate. llama.cpp can pin experts to CPU: # -ot "\\.ffn_.*_exps\\.=CPU" llama-server -hf unsloth/GLM-4.7-Flash-GGUF:Q4_K_M -c 8192 -ngl 99
Ollama
ollama run glm-4.7-flash
LM Studio
lms get unsloth/GLM-4.7-Flash-GGUF # Then load quant Q4_K_M
Assumptions
- Estimate from memory bandwidth, not a benchmark. Decode speed uses 55–75% of peak bandwidth.
- KV cache is FP16. Engine overhead assumes llama.cpp (0.3 GB + 1% of the weights). Ollama is closer to 0.9 GB + 3%, LM Studio closer to 1 GB + 3%.
- Q4_K_M size is the real GGUF file size from Hugging Face.
- 0.5 GB is reserved because this GPU also drives a display, then 5% of the rest is left free.
- Speed is quoted at 8K context, not at the maximum context that still fits.
- Mixture-of-experts split is approximate: shared layers on the GPU, expert tensors in system RAM. Speed reads more than the active-parameter slice, because shared layers stay on the bus.
Models that fit this machine more comfortably
- Qwen3 32B — Heavily degraded, UD-IQ1_S, 47–65 tok/s (estimate)
- Qwen3 VL 32B Instruct — Heavily degraded, UD-IQ1_S, 47–65 tok/s (estimate)
- GLM 4 32B 0414 — Heavily degraded, UD-IQ1_M, 48–65 tok/s (estimate)
Updated 2026-10-24. Profile on this page is the device default plus 32 GB of DDR5-5600 system RAM.