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URL: https://willitrunai.com/can-run/hf-stabilityai--stablelm-2-zephyr-1-6b-on-rx-7700s-8gb

⇱ stablelm 2 zephyr 1 6b on Radeon RX 7700S 8GB? YES

Can stablelm 2 zephyr 1 6b run on Radeon RX 7700S 8GB?

YES — Runs Great

C55Usable
○Estimated from fit model

stablelm 2 zephyr 1 6b needs ~6.1 GB VRAM. Radeon RX 7700S 8GB has 8.0 GB. With Q4_K_M quantization, expect ~46 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
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) — 6.1 GB, 46.4 tok/s, Runs well
6.1 GB required8.0 GB available
76% VRAM used

Fit status

Runs well

Decode

46.4 tok/s

TTFT

4170 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 zephyr 1 6b on Radeon RX 7700S 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: 46.4 tok/s decode · 4.2s TTFT (warm) · 116 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 well46.4 tok/s2275 ms60K
CodingCRuns well46.4 tok/s4170 ms60K
Agentic CodingCTight fit46.4 tok/s6066 ms60K
ReasoningCRuns well46.4 tok/s4928 ms60K
RAGCTight fit46.4 tok/s7582 ms60K

Quantization options

How stablelm 2 zephyr 1 6b (6B params) fits at each quantization level on Radeon RX 7700S 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.3 GB
LowC52
Q3_K_S
3
2.9 GB
LowC54
NVFP4
4
3.4 GB
MediumC54
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 zephyr 1 6b on your machine.

Run

lms load hf-stabilityai--stablelm-2-zephyr-1-6b && lms server start

Frequently asked questions

See all results for Radeon RX 7700S 8GBSee all hardware for stablelm 2 zephyr 1 6b