Can MPT-30B-Instruct run on RX 9070 XT 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

MPT-30B-Instruct needs ~47.8 GB but RX 9070 XT 16GB only has 16.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: MediumStack: BasicBottleneck: Memory capacity

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) — 47.8 GB, exceeds 16.0 GB available

47.8 GB required16.0 GB available

299% VRAM needed

31.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.9 tok/s

TTFT

66728 ms

Safe context

Memory

47.8 GB / 16.0 GB

Offload

70%

Memory breakdown

Weights21.6 GB

KV Cache23.4 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMPT-30B-Instruct on RX 9070 XT 16GB
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: 2.9 tok/s decode · 66.7s TTFT (warm) · 7 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 47.8 GB, but this setup only exposes 16.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.9 tok/s	36397 ms	4K
Coding	F	Too heavy	2.9 tok/s	66728 ms	4K
Agentic Coding	F	Too heavy	2.9 tok/s	97059 ms	4K
Reasoning	F	Too heavy	2.9 tok/s	78860 ms	4K
RAG	F	Too heavy	2.9 tok/s	121324 ms	4K

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 RX 9070 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	F0
Q3_K_S	3	14.7 GB	Low	F0
NVFP4	4	16.8 GB	Medium	F0
Q4_K_M	4	18.3 GB	Medium	F0
Q5_K_M	5	21.6 GB	High	F0
Q6_K	6	24.6 GB	High	F0
Q8_0	8	32.1 GB	Very High	F0
F16	16	61.5 GB	Maximum	F0

Opções de upgrade

Hardware que roda bem MPT-30B-Instruct

Radeon Pro W7900 48GBOpção econômica

48 GB VRAM (+32)864 GB/s (+224)

Makes the model fit on the accelerator instead of staying completely out of reach.15.9 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 448%.

~$3,999 MSRP

Radeon PRO W7900 DS 48GBMelhor custo-benefício

48 GB VRAM (+32)864 GB/s (+224)

Makes the model fit on the accelerator instead of staying completely out of reach.15.9 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 448%.

~$3,999 MSRP

AMD Instinct MI350X 288GBUpgrade AMD

288 GB VRAM (+272)8000 GB/s (+7360)

Makes the model fit on the accelerator instead of staying completely out of reach.275.8 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$8,000 MSRP

NVIDIA H100 80GBMaior salto

80 GB VRAM (+64)3350 GB/s (+2710)

Makes the model fit on the accelerator instead of staying completely out of reach.132.9 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$40,000 MSRP

Frequently asked questions

Can RX 9070 XT 16GB run MPT-30B-Instruct?

No, MPT-30B-Instruct requires more memory than RX 9070 XT 16GB provides.

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

MPT-30B-Instruct (30B parameters) requires approximately 47.8 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 RX 9070 XT 16GB?

On RX 9070 XT 16GB, MPT-30B-Instruct achieves approximately 2.9 tokens per second decode speed with a time-to-first-token of 66728ms using Q5_K_M quantization.

Can RX 9070 XT 16GB run MPT-30B-Instruct for coding?

For coding workloads, MPT-30B-Instruct on RX 9070 XT 16GB receives a F grade with 2.9 tok/s and 4K context.

What context window can MPT-30B-Instruct use on RX 9070 XT 16GB?

On RX 9070 XT 16GB, MPT-30B-Instruct can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if MPT-30B-Instruct feels slow on RX 9070 XT 16GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for RX 9070 XT 16GB See all hardware for MPT-30B-Instruct

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