Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
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VOOZH | about |
WizardMath 7B needs ~17.0 GB VRAM. NVIDIA GH200 96GB has 96.0 GB. With Q4_K_M quantization, expect ~98 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
98.0 tok/s
TTFT
1976 ms
Safe context
4K
Memory
17.0 GB / 96.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 98.0 tok/s | 1078 ms | 4K |
| Coding | B | Runs well | 98.0 tok/s | 1976 ms | 4K |
| Agentic Coding | B | Runs well | 98.0 tok/s | 2873 ms | 4K |
| Reasoning | B | Runs well | 98.0 tok/s | 2335 ms | 4K |
| RAG | B | Runs well | 98.0 tok/s | 3592 ms | 4K |
How WizardMath 7B (7B params) fits at each quantization level on NVIDIA GH200 96GB (96.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B60 |
Q3_K_S | 3 | 3.4 GB | Low | B60 |
NVFP4 | 4 | 3.9 GB | Medium | B60 |
Q4_K_M | 4 | 4.3 GB | Medium | B60 |
Q5_K_M | 5 | 5.0 GB | High | B60 |
Q6_K | 6 | 5.7 GB | High | B60 |
Q8_0 | 8 | 7.5 GB | Very High | B60 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B60 |
Copy-paste commands to run WizardMath 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "WizardLMTeam/WizardMath-7B-V1.1" \
--hf-file "WizardMath-7B-V1.1-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options