Can Magistral Small 2507 run on RX 7600 XT 16GB?

YES — With NVFP4

A78Great

Estimated from fit model

Magistral Small 2507 needs ~18.4 GB VRAM. RX 7600 XT 16GB has 16.0 GB. With NVFP4 quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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.

Magistral Small 2507 at Q4_K_M needs 19.6 GB — too much for RX 7600 XT 16GB (16.0 GB). Runs at NVFP4 (18.4 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.6 GB, exceeds 16.0 GB available

19.6 GB required16.0 GB available

123% VRAM needed

3.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.0 tok/s

TTFT

32197 ms

Safe context

Memory

19.6 GB / 16.0 GB

Offload

20%

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsMagistral Small 2507 on RX 7600 XT 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: 6.0 tok/s decode · 32.2s TTFT (warm) · 15 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 1.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Very compromised (needs ~1.9 GB host RAM)	6.9 tok/s	15337 ms	4K
Coding	F	Too heavy	6.0 tok/s	32197 ms	4K
Agentic Coding	F	Too heavy	4.7 tok/s	59973 ms	4K
Reasoning	F	Too heavy	6.0 tok/s	38051 ms	4K
RAG	F	Too heavy	4.7 tok/s	74967 ms	4K

Quantization options

How Magistral Small 2507 (24B params) fits at each quantization level on RX 7600 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	S93
Q3_K_SBest for your GPU	3	11.8 GB	Low	S92
NVFP4	4	13.4 GB	Medium	F0
Q4_K_M	4	14.6 GB	Medium	F0
Q5_K_M	5	17.3 GB	High	F0
Q6_K	6	19.7 GB	High	F0
Q8_0	8	25.7 GB	Very High	F0
F16	16	49.2 GB	Maximum	F0

Get started

Copy-paste commands to run Magistral Small 2507 on your machine.

Run

ollama run magistral

Upgrade options

Hardware that runs Magistral Small 2507 well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+512)

Makes the model fit on the accelerator instead of staying completely out of reach.35.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+8)960 GB/s (+672)

Makes the model fit on the accelerator instead of staying completely out of reach.50.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$999 MSRP

Radeon AI PRO R9700 32GBAMD upgrade

32 GB VRAM (+16)640 GB/s (+352)

Makes the model fit on the accelerator instead of staying completely out of reach.27.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,899 MSRP

Frequently asked questions

Can RX 7600 XT 16GB run Magistral Small 2507?

Yes, RX 7600 XT 16GB can run Magistral Small 2507 at NVFP4 quantization (Very compromised (needs ~1.7 GB host RAM)). The recommended Q4_K_M requires 19.6 GB which exceeds available memory, but at NVFP4 it needs only 18.4 GB. Expected decode speed: 7.9 tok/s.

How much VRAM does Magistral Small 2507 need?

Magistral Small 2507 (24B parameters) requires approximately 19.6 GB at Q4_K_M quantization. On RX 7600 XT 16GB, it fits at NVFP4 using 18.4 GB.

What is the best quantization for Magistral Small 2507?

The recommended quantization is Q4_K_M, but on RX 7600 XT 16GB the best fitting quantization is NVFP4, which uses 18.4 GB.

What speed will Magistral Small 2507 run at on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Magistral Small 2507 achieves approximately 7.9 tokens per second decode speed with a time-to-first-token of 24642ms using NVFP4 quantization.

Can RX 7600 XT 16GB run Magistral Small 2507 for coding?

For coding workloads, Magistral Small 2507 on RX 7600 XT 16GB receives a F grade with 6.0 tok/s and 4K context.

What context window can Magistral Small 2507 use on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Magistral Small 2507 can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Magistral Small 2507 feels slow on RX 7600 XT 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RX 7600 XT 16GB See all hardware for Magistral Small 2507

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Preview:

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 1.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Very compromised (needs ~1.9 GB host RAM)

6.9 tok/s

15337 ms

Coding

Too heavy

6.0 tok/s

32197 ms

Agentic Coding

Too heavy

4.7 tok/s

59973 ms

Reasoning

Too heavy

6.0 tok/s

38051 ms

RAG

Too heavy

4.7 tok/s

74967 ms

Quantization options

How Magistral Small 2507 (24B params) fits at each quantization level on RX 7600 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	S93
Q3_K_SBest for your GPU	3	11.8 GB	Low	S92
NVFP4	4	13.4 GB	Medium	F0
Q4_K_M	4	14.6 GB	Medium	F0
Q5_K_M	5	17.3 GB	High	F0
Q6_K	6	19.7 GB	High	F0
Q8_0	8	25.7 GB	Very High	F0
F16	16	49.2 GB	Maximum	F0

Frequently asked questions

Can RX 7600 XT 16GB run Magistral Small 2507?

How much VRAM does Magistral Small 2507 need?

Magistral Small 2507 (24B parameters) requires approximately 19.6 GB at Q4_K_M quantization. On RX 7600 XT 16GB, it fits at NVFP4 using 18.4 GB.

What is the best quantization for Magistral Small 2507?

The recommended quantization is Q4_K_M, but on RX 7600 XT 16GB the best fitting quantization is NVFP4, which uses 18.4 GB.

What speed will Magistral Small 2507 run at on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Magistral Small 2507 achieves approximately 7.9 tokens per second decode speed with a time-to-first-token of 24642ms using NVFP4 quantization.

Can RX 7600 XT 16GB run Magistral Small 2507 for coding?

For coding workloads, Magistral Small 2507 on RX 7600 XT 16GB receives a F grade with 6.0 tok/s and 4K context.

What context window can Magistral Small 2507 use on RX 7600 XT 16GB?

On RX 7600 XT 16GB, Magistral Small 2507 can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Magistral Small 2507 feels slow on RX 7600 XT 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.