MPT-30B-Instruct needs ~51.0 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q5_K_M quantization, expect ~16 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
15.9 tok/s
TTFT
12202 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 | 24.1 tok/s | 4387 ms | 8K |
| Coding | B | Runs with offload (needs ~1.3 GB host RAM) | 15.9 tok/s | 12202 ms | 8K |
| Agentic Coding | F | Too heavy | 7.2 tok/s | 39321 ms | 8K |
| Reasoning | B | Runs with offload (needs ~1.3 GB host RAM) | 15.9 tok/s | 14420 ms | 8K |
| RAG | F | Too heavy | 7.2 tok/s | 49152 ms | 8K |
Inference speed
Estimated decode speed (tokens/sec) for MPT-30B-Instruct at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~28 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? |
|---|---|---|---|---|
MacBook Pro M4 Max 128GB | 128 GB | Q5_K_M | 28.4 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q5_K_M | 26.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q5_K_M | 22.7 | Heavy offload |
Mac Studio M2 Ultra 128GB | 128 GB | Q5_K_M | 21.9 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q5_K_M | 20.8 | Fits |
 RTX 5090 32GB | 32 GB | Q5_K_M | 17.9 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q5_K_M | 10.5 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q5_K_M | 9.1 | Heavy offload |
MacBook Pro M1 Max 64GB | 64 GB | Q5_K_M | 8.3 | Heavy offload |
| RTX 4090 24GB | 24 GB | Q5_K_M | 6.1 | Too big |
RX 7900 XTX 24GB | 24 GB | Q5_K_M | 5.5 | Too big |
| RTX 3090 24GB | 24 GB | Q5_K_M | 5.2 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q5_K_M | 4.3 | Too big |
| RTX 4070 12GB | 12 GB | Q5_K_M | 2.7 | Too big |
| RTX 3060 12GB | 12 GB | Q5_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q5_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q5_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 MPT-30B-Instruct (30B params) fits at each quantization level on Radeon Pro W7900 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-Optionen
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 1635%.
ca. $8,000 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 231%.
ca. $10,000 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 1002%.
ca. $12,000 MSRP
Yes, Radeon Pro W7900 48GB can run MPT-30B-Instruct with a B grade (Runs with offload (needs ~1.3 GB host RAM)). Expected decode speed: 15.9 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 Radeon Pro W7900 48GB, MPT-30B-Instruct achieves approximately 15.9 tokens per second decode speed with a time-to-first-token of 12202ms using Q5_K_M quantization.
For coding workloads, MPT-30B-Instruct on Radeon Pro W7900 48GB receives a B grade with 15.9 tok/s and 8K context.
On Radeon Pro W7900 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-radeon-pro-w7900-48gb" 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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