MPT-30B-Instruct needs ~51.0 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q5_K_M quantization, expect ~18 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
3.0 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~1.3 GB host RAM)
Decode
18.2 tok/s
TTFT
10657 ms
Safe context
8K
Memory
51.0 GB / 48.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 1.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 27.6 tok/s | 3831 ms | 8K |
| Coding | B | Runs with offload (needs ~1.3 GB host RAM) | 18.2 tok/s | 10657 ms | 8K |
| Agentic Coding | F | Too heavy | 8.2 tok/s | 34342 ms | 8K |
| Reasoning | B | Runs with offload (needs ~1.3 GB host RAM) | 18.2 tok/s | 12594 ms | 8K |
| RAG | F | Too heavy | 8.2 tok/s | 42927 ms | 8K |
How MPT-30B-Instruct (30B params) fits at each quantization level on RTX A6000 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | B64 |
Q3_K_S | 3 | 14.7 GB | Low | B65 |
NVFP4 | 4 | 16.8 GB | Medium | B66 |
Q4_K_M | 4 | 18.3 GB | Medium | B66 |
Q5_K_M | 5 | 21.6 GB | High | B67 |
Q6_K | 6 | 24.6 GB | High | B68 |
Q8_0Best for your GPU | 8 | 32.1 GB | Very High | B68 |
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 99Upgrade options
NVIDIA A16 64GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$6,500 MSRP
RTX PRO 6000 Blackwell Workstation Edition 96GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 291%.
~$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 248%.
~$9,999 MSRP
Yes, RTX A6000 48GB can run MPT-30B-Instruct with a B grade (Runs with offload (needs ~1.3 GB host RAM)). Expected decode speed: 18.2 tok/s.
MPT-30B-Instruct (30B parameters) requires approximately 51.0 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 RTX A6000 48GB, MPT-30B-Instruct achieves approximately 18.2 tokens per second decode speed with a time-to-first-token of 10657ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on RTX A6000 48GB receives a B grade with 18.2 tok/s and 8K context.
On RTX A6000 48GB, 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.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mpt-30b-instruct-on-a6000-48gb" 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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