Can DeepSeek R1 Distill 7B run on Radeon Pro W7500 8GB?

YES — Tight Fit

B68Good

Estimated from fit model

DeepSeek R1 Distill 7B needs ~6.8 GB VRAM. Radeon Pro W7500 8GB has 8.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: 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) — 6.8 GB, 33.6 tok/s, Tight fit

6.8 GB required8.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

33.6 tok/s

TTFT

5761 ms

Safe context

33K

Memory

6.8 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 7B on Radeon Pro W7500 8GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 33.6 tok/s decode · 5.8s TTFT (warm) · 84 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	33.6 tok/s	3143 ms	33K
Coding	B	Tight fit	33.6 tok/s	5761 ms	33K
Agentic Coding	B	Runs with offload	33.6 tok/s	8380 ms	33K
Reasoning	B	Tight fit	33.6 tok/s	6809 ms	33K
RAG	B	Runs with offload	33.6 tok/s	10475 ms	33K

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A70
Q3_K_S	3	3.4 GB	Low	A71
NVFP4	4

Get started

Copy-paste commands to run DeepSeek R1 Distill 7B on your machine.

Run

ollama run deepseek-r1:7b

Upgrade options

Hardware that runs DeepSeek R1 Distill 7B well

RX 7700 XT 12GBBudget pick

12 GB VRAM (+4)432 GB/s (+208)

Raises estimated decode speed by about 96%.65.9 tok/s decode

Raises estimated decode speed by about 96%.

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

~$449 MSRP

RX 6700 XT 12GBBest value

12 GB VRAM (+4)384 GB/s (+160)

Raises estimated decode speed by about 51%.50.8 tok/s decode

Raises estimated decode speed by about 51%.

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

~$479 MSRP

RX 9070 16GBAMD upgrade

16 GB VRAM (+8)640 GB/s (+416)

Raises estimated decode speed by about 192%.98 tok/s decode

Raises estimated decode speed by about 192%.

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

~$479 MSRP

Frequently asked questions

Can Radeon Pro W7500 8GB run DeepSeek R1 Distill 7B?

Yes, Radeon Pro W7500 8GB can run DeepSeek R1 Distill 7B with a B grade (Tight fit). Expected decode speed: 33.6 tok/s.

How much VRAM does DeepSeek R1 Distill 7B need?

DeepSeek R1 Distill 7B (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill 7B?

The recommended quantization for DeepSeek R1 Distill 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill 7B run at on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, DeepSeek R1 Distill 7B achieves approximately 33.6 tokens per second decode speed with a time-to-first-token of 5761ms using Q4_K_M quantization.

Can Radeon Pro W7500 8GB run DeepSeek R1 Distill 7B for coding?

For coding workloads, DeepSeek R1 Distill 7B on Radeon Pro W7500 8GB receives a B grade with 33.6 tok/s and 33K context.

What context window can DeepSeek R1 Distill 7B use on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, DeepSeek R1 Distill 7B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for Radeon Pro W7500 8GB See all hardware for DeepSeek R1 Distill 7B

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