Can vntl llama3 8b v2 run on RX 6600 8GB?

Q: What should I upgrade first if vntl llama3 8b v2 feels slow on RX 6600 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — Tight Fit

C49Usable

Estimated from fit model

vntl llama3 8b v2 needs ~7.5 GB VRAM. RX 6600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 7.5 GB, 22.5 tok/s, Tight fit

7.5 GB required8.0 GB available

94% VRAM used

Fit status

Tight fit

Decode

22.5 tok/s

TTFT

8608 ms

Safe context

24K

Memory

7.5 GB / 8.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsvntl llama3 8b v2 on RX 6600 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: 22.5 tok/s decode · 8.6s TTFT (warm) · 56 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	22.5 tok/s	4695 ms	24K
Coding	C	Tight fit	22.5 tok/s	8608 ms	24K
Agentic Coding	D	Runs with offload (needs ~0.3 GB host RAM)	15.0 tok/s	18755 ms	24K
Reasoning	C	Tight fit	22.5 tok/s	10173 ms	24K
RAG	D	Runs with offload (needs ~0.3 GB host RAM)	15.0 tok/s	23444 ms

Quantization options

How vntl llama3 8b v2 (8B params) fits at each quantization level on RX 6600 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	C53
NVFP4	4

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

Upgrade options

Hardware that runs vntl llama3 8b v2 well

RX 7600 XT 16GBBudget pick

16 GB VRAM (+8)288 GB/s (+64)

Raises estimated decode speed by about 52%.34.2 tok/s decode

Raises estimated decode speed by about 52%.

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

RX 9060 XT 16GBBest value

16 GB VRAM (+8)320 GB/s (+96)

Raises estimated decode speed by about 84%.41.3 tok/s decode

Raises estimated decode speed by about 84%.

Adds memory headroom for longer context windows and future model growth.

~$349 MSRP

RX 7700 XT 12GBAMD upgrade

12 GB VRAM (+4)432 GB/s (+208)

Raises estimated decode speed by about 136%.53.1 tok/s decode

Raises estimated decode speed by about 136%.

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

Frequently asked questions

Can RX 6600 8GB run vntl llama3 8b v2?

Yes, RX 6600 8GB can run vntl llama3 8b v2 with a C grade (Tight fit). Expected decode speed: 22.5 tok/s.

How much VRAM does vntl llama3 8b v2 need?

vntl llama3 8b v2 (8B parameters) requires approximately 7.5 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 RX 6600 8GB?

On RX 6600 8GB, vntl llama3 8b v2 achieves approximately 22.5 tokens per second decode speed with a time-to-first-token of 8608ms using Q4_K_M quantization.

Can RX 6600 8GB run vntl llama3 8b v2 for coding?

For coding workloads, vntl llama3 8b v2 on RX 6600 8GB receives a C grade with 22.5 tok/s and 24K context.

What context window can vntl llama3 8b v2 use on RX 6600 8GB?

On RX 6600 8GB, vntl llama3 8b v2 can safely use up to 24K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if vntl llama3 8b v2 feels slow on RX 6600 8GB?

See all results for RX 6600 8GB See all hardware for vntl llama3 8b v2

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