MPT-30B-Instruct needs ~52.6 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q5_K_M quantization, expect ~22 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
Tight fit
Decode
22.1 tok/s
TTFT
8760 ms
Safe context
8K
Memory
52.6 GB / 64.0 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 | 22.1 tok/s | 4778 ms | 8K |
| Coding | B | Tight fit | 22.1 tok/s | 8760 ms | 8K |
| Agentic Coding | C | Very compromised (needs ~3.4 GB host RAM) | 11.5 tok/s | 24445 ms | 8K |
| Reasoning | B | Tight fit | 22.1 tok/s | 10353 ms | 8K |
| RAG | C | Very compromised (needs ~3.4 GB host RAM) | 11.5 tok/s | 30556 ms | 8K |
How MPT-30B-Instruct (30B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | B62 |
Q3_K_S | 3 | 14.7 GB | Low | B63 |
NVFP4 | 4 | 16.8 GB | Medium | B63 |
Q4_K_M | 4 | 18.3 GB | Medium | B64 |
Q5_K_M | 5 | 21.6 GB | High | B64 |
Q6_K | 6 | 24.6 GB | High | B65 |
Q8_0Best for your GPU | 8 | 32.1 GB | Very High | B67 |
F16 | 16 | 61.5 GB | Maximum | F0 |
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 99アップグレードオプション
RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ重視Raises estimated decode speed by about 222%.
Adds memory headroom for longer context windows and future model growth.
〜$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBコスパ最良Raises estimated decode speed by about 186%.
Adds memory headroom for longer context windows and future model growth.
〜$9,999 MSRP
NVIDIA H20 96GBNVIDIAアップグレードRaises estimated decode speed by about 592%.
Adds memory headroom for longer context windows and future model growth.
〜$12,000 MSRP
Yes, NVIDIA A16 64GB can run MPT-30B-Instruct with a B grade (Tight fit). Expected decode speed: 22.1 tok/s.
MPT-30B-Instruct (30B parameters) requires approximately 52.6 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 A16 64GB, MPT-30B-Instruct achieves approximately 22.1 tokens per second decode speed with a time-to-first-token of 8760ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on NVIDIA A16 64GB receives a B grade with 22.1 tok/s and 8K context.
On NVIDIA A16 64GB, 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-a16-64gb" 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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