Can DeepSeek R1 0528 Qwen3 8B run on RTX 2070 Super 8GB?

Q: What should I upgrade first if DeepSeek R1 0528 Qwen3 8B feels slow on RTX 2070 Super 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

C52Usable

Estimated from fit model

DeepSeek R1 0528 Qwen3 8B needs ~7.8 GB VRAM. RTX 2070 Super 8GB has 8.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: 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

Q4_K_M (Medium quality) — 7.8 GB, 56.0 tok/s, Runs with offload

7.8 GB required8.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

56.0 tok/s

TTFT

3457 ms

Safe context

19K

Memory

7.8 GB / 8.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsDeepSeek R1 0528 Qwen3 8B on RTX 2070 Super 8GB
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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	56.0 tok/s	1886 ms	19K
Coding	C	Runs with offload	56.0 tok/s	3457 ms	19K
Agentic Coding	C	Very compromised	33.5 tok/s	8404 ms	19K
Reasoning	C	Runs with offload	56.0 tok/s	4086 ms	19K
RAG	C	Very compromised (needs ~0.4 GB host RAM)	33.5 tok/s	10505 ms	19K

Quantization options

How DeepSeek R1 0528 Qwen3 8B (8B params) fits at each quantization level on RTX 2070 Super 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	C53
NVFP4	4

Get started

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

Run

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

Upgrade options

Hardware that runs DeepSeek R1 0528 Qwen3 8B well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.48.7 tok/s decode

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

~$329 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+4)672 GB/s (+224)

Raises estimated decode speed by about 55%.86.8 tok/s decode

Raises estimated decode speed by about 55%.

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

~$549 MSRP

RTX 4070 Super 12GBNVIDIA upgrade

12 GB VRAM (+4)504 GB/s (+56)

Raises estimated decode speed by about 42%.79.5 tok/s decode

Raises estimated decode speed by about 42%.

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

~$599 MSRP

Frequently asked questions

Can RTX 2070 Super 8GB run DeepSeek R1 0528 Qwen3 8B?

Yes, RTX 2070 Super 8GB can run DeepSeek R1 0528 Qwen3 8B with a C grade (Runs with offload). Expected decode speed: 56.0 tok/s.

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

DeepSeek R1 0528 Qwen3 8B (8B parameters) requires approximately 7.8 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 RTX 2070 Super 8GB?

On RTX 2070 Super 8GB, DeepSeek R1 0528 Qwen3 8B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.

Can RTX 2070 Super 8GB run DeepSeek R1 0528 Qwen3 8B for coding?

For coding workloads, DeepSeek R1 0528 Qwen3 8B on RTX 2070 Super 8GB receives a C grade with 56.0 tok/s and 19K context.

What context window can DeepSeek R1 0528 Qwen3 8B use on RTX 2070 Super 8GB?

On RTX 2070 Super 8GB, DeepSeek R1 0528 Qwen3 8B can safely use up to 19K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 2070 Super 8GB See all hardware for DeepSeek R1 0528 Qwen3 8B

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