MPT-30B-Instruct needs ~54.2 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q5_K_M quantization, expect ~81 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
80.9 tok/s
TTFT
2394 ms
Safe context
8K
Memory
54.2 GB / 80.0 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 | A | Runs well | 80.9 tok/s | 1306 ms | 8K |
| Coding | A | Runs well | 80.9 tok/s | 2394 ms | 8K |
| Agentic Coding | A | Runs with offload | 80.9 tok/s | 3482 ms | 8K |
| Reasoning | A | Runs well | 80.9 tok/s | 2829 ms | 8K |
| RAG | A | Runs with offload | 80.9 tok/s | 4352 ms | 8K |
How MPT-30B-Instruct (30B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | B61 |
Q3_K_S | 3 | 14.7 GB | Low | B62 |
NVFP4 | 4 | 16.8 GB | Medium | B62 |
Q4_K_M | 4 | 18.3 GB | Medium | B62 |
Q5_K_M | 5 | 21.6 GB | High | B63 |
Q6_K | 6 | 24.6 GB | High | B63 |
Q8_0 | 8 | 32.1 GB | Very High | B65 |
F16Best for your GPU | 16 | 61.5 GB | Maximum | B68 |
Copy-paste commands to run MPT-30B-Instruct on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "mosaicml/mpt-30b-instruct" \
--hf-file "mpt-30b-instruct-Q5_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Devstral 2 123B Instruct | 123B | A | 17.6 tok/s | |
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 259 tok/s | |
| Qwen 3.5 122B A10B | 122B | A | 52.1 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 217.7 tok/s | |
| Qwen 3.5 35B A3B | 35B | S | 236.7 tok/s |
Yes, NVIDIA A100 80GB can run MPT-30B-Instruct with a A grade (Runs well). Expected decode speed: 80.9 tok/s.
MPT-30B-Instruct (30B parameters) requires approximately 54.2 GB of memory with Q5_K_M quantization.
The recommended quantization for MPT-30B-Instruct is Q5_K_M, which balances quality and memory efficiency.
On NVIDIA A100 80GB, MPT-30B-Instruct achieves approximately 80.9 tokens per second decode speed with a time-to-first-token of 2394ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on NVIDIA A100 80GB receives a A grade with 80.9 tok/s and 8K context.
On NVIDIA A100 80GB, MPT-30B-Instruct can safely use up to 8K tokens of context. The model's official context limit is 8K, 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/mpt-30b-instruct-on-a100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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