Can DeepSeek LLM 7B run on Radeon RX 7900M 16GB?

YES — Tight Fit

C52Usable

Estimated from fit model

DeepSeek LLM 7B needs ~14.1 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~80 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: 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) — 14.1 GB, 79.6 tok/s, Tight fit

14.1 GB required16.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

79.6 tok/s

TTFT

2433 ms

Safe context

Memory

14.1 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsDeepSeek LLM 7B on Radeon RX 7900M 16GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 79.6 tok/s decode · 2.4s TTFT (warm) · 199 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	79.6 tok/s	1327 ms	4K
Coding	C	Tight fit	79.6 tok/s	2433 ms	4K
Agentic Coding	F	Too heavy	32.3 tok/s	8717 ms	4K
Reasoning	C	Tight fit	79.6 tok/s	2875 ms	4K
RAG	F	Too heavy	32.3 tok/s	10896 ms	4K

Quantization options

How DeepSeek LLM 7B (7B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C45
Q3_K_S	3	3.4 GB	Low	C46
NVFP4	4

Get started

Copy-paste commands to run DeepSeek LLM 7B on your machine.

Run

ollama run deepseek-llm

Upgrade options

Hardware that runs DeepSeek LLM 7B well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+224)

Adds memory headroom for longer context windows and future model growth.98 tok/s decode

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

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+8)960 GB/s (+384)

Adds memory headroom for longer context windows and future model growth.98 tok/s decode

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

~$999 MSRP

Frequently asked questions

Can Radeon RX 7900M 16GB run DeepSeek LLM 7B?

Yes, Radeon RX 7900M 16GB can run DeepSeek LLM 7B with a C grade (Tight fit). Expected decode speed: 79.6 tok/s.

How much VRAM does DeepSeek LLM 7B need?

DeepSeek LLM 7B (7B parameters) requires approximately 14.1 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek LLM 7B?

The recommended quantization for DeepSeek LLM 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek LLM 7B run at on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, DeepSeek LLM 7B achieves approximately 79.6 tokens per second decode speed with a time-to-first-token of 2433ms using Q4_K_M quantization.

Can Radeon RX 7900M 16GB run DeepSeek LLM 7B for coding?

For coding workloads, DeepSeek LLM 7B on Radeon RX 7900M 16GB receives a C grade with 79.6 tok/s and 4K context.

What context window can DeepSeek LLM 7B use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, DeepSeek LLM 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for Radeon RX 7900M 16GB See all hardware for DeepSeek LLM 7B

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