Will It Run AI

Can stablelm 2 1 6b chat imatrix run on RX 6600 XT 8GB?

YES — Runs Great

C54Usable
Estimated from fit model

stablelm 2 1 6b chat imatrix needs ~6.1 GB VRAM. RX 6600 XT 8GB has 8.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 6.1 GB, 35.0 tok/s, Runs well
6.1 GB required8.0 GB available
76% VRAM used

Fit status

Runs well

Decode

35.0 tok/s

TTFT

5536 ms

Safe context

60K

Memory

6.1 GB / 8.0 GB

Memory breakdown

Weights3.7 GB
KV Cache0.7 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsstablelm 2 1 6b chat imatrix on RX 6600 XT 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: 35.0 tok/s decode · 5.5s TTFT (warm) · 87 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 well35.0 tok/s3020 ms60K
CodingCRuns well35.0 tok/s5536 ms60K
Agentic CodingCTight fit35.0 tok/s8052 ms60K
ReasoningCRuns well35.0 tok/s6542 ms60K
RAGCTight fit35.0 tok/s10065 ms60K

Quantization options

How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on RX 6600 XT 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.3 GB
LowC52
Q3_K_S
3
2.9 GB
LowC53
NVFP4
4
3.4 GB
MediumC53
Q4_K_M
4
3.7 GB
MediumC53
Q5_K_M
5
4.3 GB
HighC53
Q6_KBest for your GPU
6
4.9 GB
HighC53
Q8_0
8
6.4 GB
Very HighF0
F16
16
12.3 GB
MaximumF0

Get started

Copy-paste commands to run stablelm 2 1 6b chat imatrix on your machine.

Run

lms load hf-crataco--stablelm-2-1-6b-chat-imatrix-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien stablelm 2 1 6b chat imatrix

IntelIntel Arc B570 10GBOpción económica
10 GB VRAM (+2)380 GB/s (+124)
C
Sube la velocidad estimada de decodificación alrededor de un 60%.56.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 60%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$219 MSRP

NVIDIARTX 3080 10GBMejor relación calidad-precio
10 GB VRAM (+2)760 GB/s (+504)
C
Sube la velocidad estimada de decodificación alrededor de un 106%.72 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 106%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$699 MSRP

Frequently asked questions

Can RX 6600 XT 8GB run stablelm 2 1 6b chat imatrix?

Yes, RX 6600 XT 8GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 35.0 tok/s.

How much VRAM does stablelm 2 1 6b chat imatrix need?

stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 6.1 GB of memory with Q4_K_M quantization.

What is the best quantization for stablelm 2 1 6b chat imatrix?

The recommended quantization for stablelm 2 1 6b chat imatrix is Q4_K_M, which balances quality and memory efficiency.

What speed will stablelm 2 1 6b chat imatrix run at on RX 6600 XT 8GB?

On RX 6600 XT 8GB, stablelm 2 1 6b chat imatrix achieves approximately 35.0 tokens per second decode speed with a time-to-first-token of 5536ms using Q4_K_M quantization.

Can RX 6600 XT 8GB run stablelm 2 1 6b chat imatrix for coding?

For coding workloads, stablelm 2 1 6b chat imatrix on RX 6600 XT 8GB receives a C grade with 35.0 tok/s and 60K context.

What context window can stablelm 2 1 6b chat imatrix use on RX 6600 XT 8GB?

On RX 6600 XT 8GB, stablelm 2 1 6b chat imatrix can safely use up to 60K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 6600 XT 8GBSee all hardware for stablelm 2 1 6b chat imatrix
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