Can MPT-30B-Instruct run on NVIDIA A16 64GB?

YES — Tight Fit

B70Good

Estimated from fit model

MPT-30B-Instruct needs ~52.6 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q5_K_M quantization, expect ~22 tok/s.

Runtime: OllamaCapacity: TightBandwidth: MediumStack: 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) — 52.6 GB, 22.1 tok/s, Tight fit

52.6 GB required64.0 GB available

82% VRAM used

Fit status

Tight fit

Decode

22.1 tok/s

TTFT

8760 ms

Safe context

Memory

52.6 GB / 64.0 GB

Memory breakdown

Weights21.6 GB

KV Cache23.4 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsMPT-30B-Instruct on NVIDIA A16 64GB
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: 22.1 tok/s decode · 8.8s TTFT (warm) · 55 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	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

Quantization options

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

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

Upgrade-Optionen

Hardware, die MPT-30B-Instruct gut ausführt

RTX PRO 6000 Blackwell Workstation Edition 96GBGünstige Wahl

96 GB VRAM (+32)1792 GB/s (+1192)

Raises estimated decode speed by about 222%.71.1 tok/s Dekodierung

Raises estimated decode speed by about 222%.

Adds memory headroom for longer context windows and future model growth.

ca. $9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBBestes Preis-Leistungs-Verhältnis

96 GB VRAM (+32)1597 GB/s (+997)

Raises estimated decode speed by about 186%.63.3 tok/s Dekodierung

Raises estimated decode speed by about 186%.

Adds memory headroom for longer context windows and future model growth.

ca. $9,999 MSRP

NVIDIA H20 96GBNVIDIA-Upgrade

96 GB VRAM (+32)4000 GB/s (+3400)

Raises estimated decode speed by about 592%.153 tok/s Dekodierung

Raises estimated decode speed by about 592%.

Adds memory headroom for longer context windows and future model growth.

ca. $12,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run MPT-30B-Instruct?

Yes, NVIDIA A16 64GB can run MPT-30B-Instruct with a B grade (Tight fit). Expected decode speed: 22.1 tok/s.

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

MPT-30B-Instruct (30B parameters) requires approximately 52.6 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 NVIDIA A16 64GB?

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.

Can NVIDIA A16 64GB run MPT-30B-Instruct for coding?

For coding workloads, MPT-30B-Instruct on NVIDIA A16 64GB receives a B grade with 22.1 tok/s and 8K context.

What context window can MPT-30B-Instruct use on NVIDIA A16 64GB?

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.

See all results for NVIDIA A16 64GB See all hardware for MPT-30B-Instruct

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