Can WizardMath 7B run on RX 5600 XT 6GB?

Q: What should I upgrade first if WizardMath 7B feels slow on RX 5600 XT 6GB?

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.

YES — With Q3_K_S

B60Good

Estimated from fit model

WizardMath 7B needs ~6.9 GB VRAM. RX 5600 XT 6GB has 6.0 GB. With Q3_K_S quantization, expect ~25 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.

WizardMath 7B at Q4_K_M needs 7.7 GB — too much for RX 5600 XT 6GB (6.0 GB). Runs at Q3_K_S (6.9 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.7 GB, exceeds 6.0 GB available

7.7 GB required6.0 GB available

128% VRAM needed

1.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

16.6 tok/s

TTFT

11648 ms

Safe context

Memory

7.7 GB / 6.0 GB

Offload

20%

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsWizardMath 7B on RX 5600 XT 6GB
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: 16.6 tok/s decode · 11.6s TTFT (warm) · 42 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 0.4 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	B	Very compromised (needs ~0.5 GB host RAM)	22.1 tok/s	4780 ms	4K
Coding	F	Too heavy	16.6 tok/s	11648 ms	4K
Agentic Coding	F	Too heavy	10.3 tok/s	27233 ms	4K
Reasoning	F	Too heavy	16.6 tok/s	13766 ms	4K
RAG	F	Too heavy	9.6 tok/s	36595 ms	4K

Quantization options

How WizardMath 7B (7B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A75
Q3_K_SBest for your GPU	3	3.4 GB	Low	A74

Get started

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

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "WizardLMTeam/WizardMath-7B-V1.1" \
  --hf-file "WizardMath-7B-V1.1-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs WizardMath 7B well

RX 580 8GBBudget pick

8 GB VRAM (+2)

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.

~$229 MSRP

RX 9060 8GBBest value

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.45.7 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.

~$249 MSRP

RX 7600 8GBAMD upgrade

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.42.1 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.

~$269 MSRP

RTX 2080 Ti 11GBBiggest leap

11 GB VRAM (+5)616 GB/s (+328)

Makes the model fit on the accelerator instead of staying completely out of reach.98 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

Frequently asked questions

Can RX 5600 XT 6GB run WizardMath 7B?

Yes, RX 5600 XT 6GB can run WizardMath 7B at Q3_K_S quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 7.7 GB which exceeds available memory, but at Q3_K_S it needs only 6.9 GB. Expected decode speed: 24.5 tok/s.

How much VRAM does WizardMath 7B need?

WizardMath 7B (7B parameters) requires approximately 7.7 GB at Q4_K_M quantization. On RX 5600 XT 6GB, it fits at Q3_K_S using 6.9 GB.

What is the best quantization for WizardMath 7B?

The recommended quantization is Q4_K_M, but on RX 5600 XT 6GB the best fitting quantization is Q3_K_S, which uses 6.9 GB.

What speed will WizardMath 7B run at on RX 5600 XT 6GB?

On RX 5600 XT 6GB, WizardMath 7B achieves approximately 24.5 tokens per second decode speed with a time-to-first-token of 7896ms using Q3_K_S quantization.

Can RX 5600 XT 6GB run WizardMath 7B for coding?

For coding workloads, WizardMath 7B on RX 5600 XT 6GB receives a F grade with 16.6 tok/s and 4K context.

What context window can WizardMath 7B use on RX 5600 XT 6GB?

On RX 5600 XT 6GB, WizardMath 7B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for RX 5600 XT 6GB See all hardware for WizardMath 7B

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