WizardMath 7B needs ~35.9 GB VRAM. AMD Instinct MI350X 288GB has 288.0 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
35.9 GB / 288.0 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 |
Inference speed
Estimated decode speed (tokens/sec) for WizardMath 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| 32 GB | Q4_K_M | 98.0 | Fits | |
| 24 GB | Q4_K_M | 98.0 | Fits | |
| 16 GB | Q4_K_M | 98.0 | Fits | |
| 24 GB | Q4_K_M | 98.0 | Fits | |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 98.0 | Fits |
| 12 GB | Q4_K_M | 95.2 | Fits | |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 94.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 94.4 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 60.4 | Fits |
| 12 GB | Q4_K_M | 59.8 | Fits | |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 55.4 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 48.7 | Fits |
| 8 GB | Q4_K_M | 46.0 | Offloads |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How WizardMath 7B (7B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B56 |
Q3_K_S | 3 | 3.4 GB | Low | B56 |
NVFP4 | 4 | 3.9 GB | Medium | B56 |
Q4_K_M | 4 | 4.3 GB | Medium | B56 |
Q5_K_M | 5 | 5.0 GB | High | B56 |
Q6_K | 6 | 5.7 GB | High | B56 |
Q8_0 | 8 | 7.5 GB | Very High | B56 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B57 |
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 99Yes, AMD Instinct MI350X 288GB can run WizardMath 7B with a B grade (Runs well). Expected decode speed: 98.0 tok/s.
WizardMath 7B (7B parameters) requires approximately 35.9 GB of memory with Q4_K_M quantization.
The recommended quantization for WizardMath 7B is Q4_K_M, which balances quality and memory efficiency.
On AMD Instinct MI350X 288GB, WizardMath 7B achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.
For coding workloads, WizardMath 7B on AMD Instinct MI350X 288GB receives a B grade with 98.0 tok/s and 4K context.
On AMD Instinct MI350X 288GB, WizardMath 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/wizard-math-7b-on-instinct-mi350x-288gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: