Can DeepSeek R1 0528 Qwen3 8B run on NVIDIA A2 16GB?

YES — Runs Great

C50Usable

Estimated from fit model

DeepSeek R1 0528 Qwen3 8B needs ~8.6 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~32 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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

Q4_K_M (Medium quality) — 8.6 GB, 32.0 tok/s, Runs well

8.6 GB required16.0 GB available

54% VRAM used

Fit status

Runs well

Decode

32.0 tok/s

TTFT

6056 ms

Safe context

142K

Memory

8.6 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsDeepSeek R1 0528 Qwen3 8B on NVIDIA A2 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: 32.0 tok/s decode · 6.1s TTFT (warm) · 80 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	C	Runs well	32.0 tok/s	3303 ms	142K
Coding	C	Runs well	32.0 tok/s	6056 ms	142K
Agentic Coding	C	Runs well	32.0 tok/s	8809 ms	142K
Reasoning	C	Runs well	32.0 tok/s	7157 ms	142K
RAG	C	Runs well	32.0 tok/s	11011 ms	142K

Quantization options

How DeepSeek R1 0528 Qwen3 8B (8B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C48
NVFP4	4

Get started

Copy-paste commands to run DeepSeek R1 0528 Qwen3 8B on your machine.

Run

lms load hf-lmstudio-community--deepseek-r1-0528-qwen3-8b-gguf && lms server start

Upgrade options

Hardware that runs DeepSeek R1 0528 Qwen3 8B well

RTX 4000 Ada 20GBBudget pick

20 GB VRAM (+4)360 GB/s (+160)

Raises estimated decode speed by about 80%.57.5 tok/s decode

Raises estimated decode speed by about 80%.

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

~$1,250 MSRP

RTX 3090 24GBBest value

24 GB VRAM (+8)936 GB/s (+736)

Raises estimated decode speed by about 250%.112 tok/s decode

Raises estimated decode speed by about 250%.

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

~$1,499 MSRP

RTX PRO 4000 Blackwell 24GBNVIDIA upgrade

24 GB VRAM (+8)672 GB/s (+472)

Raises estimated decode speed by about 250%.112 tok/s decode

Raises estimated decode speed by about 250%.

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

~$1,599 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run DeepSeek R1 0528 Qwen3 8B?

Yes, NVIDIA A2 16GB can run DeepSeek R1 0528 Qwen3 8B with a C grade (Runs well). Expected decode speed: 32.0 tok/s.

How much VRAM does DeepSeek R1 0528 Qwen3 8B need?

DeepSeek R1 0528 Qwen3 8B (8B parameters) requires approximately 8.6 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 0528 Qwen3 8B?

The recommended quantization for DeepSeek R1 0528 Qwen3 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 0528 Qwen3 8B run at on NVIDIA A2 16GB?

On NVIDIA A2 16GB, DeepSeek R1 0528 Qwen3 8B achieves approximately 32.0 tokens per second decode speed with a time-to-first-token of 6056ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run DeepSeek R1 0528 Qwen3 8B for coding?

For coding workloads, DeepSeek R1 0528 Qwen3 8B on NVIDIA A2 16GB receives a C grade with 32.0 tok/s and 142K context.

What context window can DeepSeek R1 0528 Qwen3 8B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, DeepSeek R1 0528 Qwen3 8B can safely use up to 142K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A2 16GB See all hardware for DeepSeek R1 0528 Qwen3 8B

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