Will It Run AI

Can Llama 3 8B Instruct 32k v0.1 run on RX 9070 16GB?

YES — Runs Great

C53Usable
Estimated from fit model

Llama 3 8B Instruct 32k v0.1 needs ~8.3 GB VRAM. RX 9070 16GB has 16.0 GB. With Q4_K_M quantization, expect ~81 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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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) — 8.3 GB, 81.3 tok/s, Runs well
8.3 GB required16.0 GB available
52% VRAM used

Fit status

Runs well

Decode

81.3 tok/s

TTFT

2381 ms

Safe context

147K

Memory

8.3 GB / 16.0 GB

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsLlama 3 8B Instruct 32k v0.1 on RX 9070 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: 81.3 tok/s decode · 2.4s TTFT (warm) · 203 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well81.3 tok/s1299 ms147K
CodingCRuns well81.3 tok/s2381 ms147K
Agentic CodingCRuns well81.3 tok/s3463 ms147K
ReasoningCRuns well81.3 tok/s2814 ms147K
RAGCRuns well81.3 tok/s4329 ms147K

Quantization options

How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on RX 9070 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC47
Q3_K_S
3
3.9 GB
LowC48
NVFP4
4
4.5 GB
MediumC48
Q4_K_M
4
4.9 GB
MediumC49
Q5_K_M
5
5.8 GB
HighC50
Q6_K
6
6.6 GB
HighC51
Q8_0Best for your GPU
8
8.6 GB
Very HighC52
F16
16
16.4 GB
MaximumF0

Get started

Copy-paste commands to run Llama 3 8B Instruct 32k v0.1 on your machine.

Run

lms load hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf && lms server start

Frequently asked questions

Can RX 9070 16GB run Llama 3 8B Instruct 32k v0.1?

Yes, RX 9070 16GB can run Llama 3 8B Instruct 32k v0.1 with a C grade (Runs well). Expected decode speed: 81.3 tok/s.

How much VRAM does Llama 3 8B Instruct 32k v0.1 need?

Llama 3 8B Instruct 32k v0.1 (8B parameters) requires approximately 8.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3 8B Instruct 32k v0.1?

The recommended quantization for Llama 3 8B Instruct 32k v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3 8B Instruct 32k v0.1 run at on RX 9070 16GB?

On RX 9070 16GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 81.3 tokens per second decode speed with a time-to-first-token of 2381ms using Q4_K_M quantization.

Can RX 9070 16GB run Llama 3 8B Instruct 32k v0.1 for coding?

For coding workloads, Llama 3 8B Instruct 32k v0.1 on RX 9070 16GB receives a C grade with 81.3 tok/s and 147K context.

What context window can Llama 3 8B Instruct 32k v0.1 use on RX 9070 16GB?

On RX 9070 16GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 147K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 9070 16GBSee all hardware for Llama 3 8B Instruct 32k v0.1
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