Can vntl llama3 8b v2 run on Radeon RX 7900M 16GB?

YES — Runs Great

C52Usable

Estimated from fit model

vntl llama3 8b v2 needs ~8.3 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~70 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced

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, 69.6 tok/s, Runs well

8.3 GB required16.0 GB available

52% VRAM used

Fit status

Runs well

Decode

69.6 tok/s

TTFT

2780 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 feelsvntl llama3 8b v2 on Radeon RX 7900M 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: 69.6 tok/s decode · 2.8s TTFT (warm) · 174 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	C	Runs well	69.6 tok/s	1516 ms	147K
Coding	C	Runs well	69.6 tok/s	2780 ms	147K
Agentic Coding	C	Runs well	69.6 tok/s	4044 ms	147K
Reasoning	C	Runs well	69.6 tok/s	3285 ms	147K
RAG	C	Runs well	69.6 tok/s	5055 ms	147K

Quantization options

How vntl llama3 8b v2 (8B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C48
NVFP4	4	4.5 GB	Medium	C48
Q4_K_M	4	4.9 GB	Medium	C49
Q5_K_M	5	5.8 GB	High	C50
Q6_K	6	6.6 GB	High	C51
Q8_0Best for your GPU	8	8.6 GB	Very High	C52
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run vntl llama3 8b v2 on your machine.

Run

lms load hf-lmg-anon--vntl-llama3-8b-v2-gguf && lms server start

Frequently asked questions

Can Radeon RX 7900M 16GB run vntl llama3 8b v2?

Yes, Radeon RX 7900M 16GB can run vntl llama3 8b v2 with a C grade (Runs well). Expected decode speed: 69.6 tok/s.

How much VRAM does vntl llama3 8b v2 need?

vntl llama3 8b v2 (8B parameters) requires approximately 8.3 GB of memory with Q4_K_M quantization.

What is the best quantization for vntl llama3 8b v2?

The recommended quantization for vntl llama3 8b v2 is Q4_K_M, which balances quality and memory efficiency.

What speed will vntl llama3 8b v2 run at on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, vntl llama3 8b v2 achieves approximately 69.6 tokens per second decode speed with a time-to-first-token of 2780ms using Q4_K_M quantization.

Can Radeon RX 7900M 16GB run vntl llama3 8b v2 for coding?

For coding workloads, vntl llama3 8b v2 on Radeon RX 7900M 16GB receives a C grade with 69.6 tok/s and 147K context.

What context window can vntl llama3 8b v2 use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, vntl llama3 8b v2 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 Radeon RX 7900M 16GB See all hardware for vntl llama3 8b v2

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<iframe src="https://willitrunai.com/embed/hf-lmg-anon--vntl-llama3-8b-v2-gguf-on-rx-7900m-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

69.6 tok/s

1516 ms

147K

Coding

Runs well

69.6 tok/s

2780 ms

147K

Agentic Coding

Runs well

69.6 tok/s

4044 ms

147K

Reasoning

Runs well

69.6 tok/s

3285 ms

147K

RAG

Runs well

69.6 tok/s

5055 ms

147K

Quantization options

How vntl llama3 8b v2 (8B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C48
NVFP4	4	4.5 GB	Medium	C48
Q4_K_M	4	4.9 GB	Medium	C49
Q5_K_M	5	5.8 GB	High	C50
Q6_K	6	6.6 GB	High	C51
Q8_0Best for your GPU	8	8.6 GB	Very High	C52
F16	16	16.4 GB	Maximum	F0

Frequently asked questions

Can Radeon RX 7900M 16GB run vntl llama3 8b v2?

Yes, Radeon RX 7900M 16GB can run vntl llama3 8b v2 with a C grade (Runs well). Expected decode speed: 69.6 tok/s.

How much VRAM does vntl llama3 8b v2 need?

vntl llama3 8b v2 (8B parameters) requires approximately 8.3 GB of memory with Q4_K_M quantization.

What is the best quantization for vntl llama3 8b v2?

The recommended quantization for vntl llama3 8b v2 is Q4_K_M, which balances quality and memory efficiency.

What speed will vntl llama3 8b v2 run at on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, vntl llama3 8b v2 achieves approximately 69.6 tokens per second decode speed with a time-to-first-token of 2780ms using Q4_K_M quantization.

Can Radeon RX 7900M 16GB run vntl llama3 8b v2 for coding?

For coding workloads, vntl llama3 8b v2 on Radeon RX 7900M 16GB receives a C grade with 69.6 tok/s and 147K context.

What context window can vntl llama3 8b v2 use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, vntl llama3 8b v2 can safely use up to 147K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.