Can MPT-30B-Instruct run on AMD Instinct MI350X 288GB?

YES — Runs Great

B68Good

Estimated from fit model

MPT-30B-Instruct needs ~75.0 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q5_K_M quantization, expect ~276 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

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.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 75.0 GB, 275.8 tok/s, Runs well

75.0 GB required288.0 GB available

26% VRAM used

Fit status

Runs well

Decode

275.8 tok/s

TTFT

702 ms

Safe context

Memory

75.0 GB / 288.0 GB

Memory breakdown

Weights21.6 GB

KV Cache23.4 GB

Runtime1.2 GB

Headroom28.8 GB

See how fast it feels

See how fast it feelsMPT-30B-Instruct on AMD Instinct MI350X 288GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 275.8 tok/s decode · 702ms TTFT (warm) · 689 tok/s prefill

What limits this setup

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.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	275.8 tok/s	383 ms	8K
Coding	B	Runs well	275.8 tok/s	702 ms	8K
Agentic Coding	B	Runs well	275.8 tok/s	1021 ms	8K
Reasoning	B	Runs well	275.8 tok/s	830 ms	8K
RAG	B	Runs well	275.8 tok/s	1276 ms	8K

Inference speed

MPT-30B-Instruct inference speed — tokens per second by GPU & Mac

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.

Quantization options

How MPT-30B-Instruct (30B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	B57
Q3_K_S	3	14.7 GB	Low	B57
NVFP4	4	16.8 GB	Medium	B57
Q4_K_M	4	18.3 GB	Medium	B57
Q5_K_M	5	21.6 GB	High	B57
Q6_K	6	24.6 GB	High	B58
Q8_0	8	32.1 GB	Very High	B58
F16Best for your GPU	16	61.5 GB	Maximum	B60

Get started

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

Frequently asked questions

Can AMD Instinct MI350X 288GB run MPT-30B-Instruct?

Yes, AMD Instinct MI350X 288GB can run MPT-30B-Instruct with a B grade (Runs well). Expected decode speed: 275.8 tok/s.

How much VRAM does MPT-30B-Instruct need?

MPT-30B-Instruct (30B parameters) requires approximately 75.0 GB of memory with Q5_K_M quantization.

What is the best quantization for MPT-30B-Instruct?

The recommended quantization for MPT-30B-Instruct is Q5_K_M, which balances quality and memory efficiency.

What speed will MPT-30B-Instruct run at on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, MPT-30B-Instruct achieves approximately 275.8 tokens per second decode speed with a time-to-first-token of 702ms using Q5_K_M quantization.

Can AMD Instinct MI350X 288GB run MPT-30B-Instruct for coding?

For coding workloads, MPT-30B-Instruct on AMD Instinct MI350X 288GB receives a B grade with 275.8 tok/s and 8K context.

What context window can MPT-30B-Instruct use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, 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.

See all results for AMD Instinct MI350X 288GB See all hardware for MPT-30B-Instruct

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