Will It Run AI

Can Llama 3.1 8B run on Radeon Pro W7500 8GB?

YES — With Offload

B60Good
Estimated from fit model

Llama 3.1 8B needs ~8.5 GB VRAM. Radeon Pro W7500 8GB has 8.0 GB. With Q4_K_M quantization, expect ~19 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload
Share:

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) — 8.5 GB, 19.1 tok/s, Runs with offload (needs ~0.3 GB host RAM)
8.5 GB required8.0 GB available
106% VRAM needed

0.5 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.3 GB host RAM)

Decode

19.1 tok/s

TTFT

10156 ms

Safe context

12K

Memory

8.5 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsLlama 3.1 8B on Radeon Pro W7500 8GB
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: 19.1 tok/s decode · 10.2s TTFT (warm) · 48 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 0.3 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatATight fit27.1 tok/s3899 ms12K
CodingBRuns with offload (needs ~0.3 GB host RAM)19.1 tok/s10156 ms12K
Agentic CodingFToo heavy12.4 tok/s22797 ms12K
ReasoningBRuns with offload (needs ~0.3 GB host RAM)19.1 tok/s12003 ms12K
RAGFToo heavy12.4 tok/s28497 ms12K

Quantization options

How Llama 3.1 8B (8B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowA75
Q3_K_S
3
3.9 GB
LowA74
NVFP4
4
4.5 GB
MediumA74
Q4_K_MBest for your GPU
4
4.9 GB
MediumA74
Q5_K_M
5
5.8 GB
HighF0
Q6_K
6
6.6 GB
HighF0
Q8_0
8
8.6 GB
Very HighF0
F16
16
16.4 GB
MaximumF0

Get started

Copy-paste commands to run Llama 3.1 8B on your machine.

Run

ollama run llama3.1

Upgrade-Optionen

Hardware, die Llama 3.1 8B gut ausführt

RX 7600 XT 16GBGünstige Wahl
16 GB VRAM (+8)288 GB/s (+64)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.36.8 tok/s Dekodierung

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

Raises estimated decode speed by about 93%.

ca. $329 MSRP

RX 9060 XT 16GBBestes Preis-Leistungs-Verhältnis
16 GB VRAM (+8)320 GB/s (+96)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.44.4 tok/s Dekodierung

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

Raises estimated decode speed by about 132%.

ca. $349 MSRP

RX 7700 XT 12GBAMD-Upgrade
12 GB VRAM (+4)432 GB/s (+208)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.57.1 tok/s Dekodierung

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

Raises estimated decode speed by about 199%.

ca. $449 MSRP

Frequently asked questions

Can Radeon Pro W7500 8GB run Llama 3.1 8B?

Yes, Radeon Pro W7500 8GB can run Llama 3.1 8B with a B grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 19.1 tok/s.

How much VRAM does Llama 3.1 8B need?

Llama 3.1 8B (8B parameters) requires approximately 8.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.1 8B?

The recommended quantization for Llama 3.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.1 8B run at on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, Llama 3.1 8B achieves approximately 19.1 tokens per second decode speed with a time-to-first-token of 10156ms using Q4_K_M quantization.

Can Radeon Pro W7500 8GB run Llama 3.1 8B for coding?

For coding workloads, Llama 3.1 8B on Radeon Pro W7500 8GB receives a B grade with 19.1 tok/s and 12K context.

What context window can Llama 3.1 8B use on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, Llama 3.1 8B can safely use up to 12K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Llama 3.1 8B feels slow on Radeon Pro W7500 8GB?

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 Radeon Pro W7500 8GBSee all hardware for Llama 3.1 8B
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/llama-3.1-8b-on-radeon-pro-w7500-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: