willitrun·ai

Can stablelm 2 zephyr 1 6b run on RX 7600 XT 16GB?

YES — Runs Great

C49Usable
Estimated from fit model

stablelm 2 zephyr 1 6b needs ~6.9 GB VRAM. RX 7600 XT 16GB has 16.0 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) — 6.9 GB, 45.6 tok/s, Runs well
6.9 GB required16.0 GB available
43% VRAM used

Fit status

Runs well

Decode

45.6 tok/s

TTFT

4242 ms

Safe context

224K

Memory

6.9 GB / 16.0 GB

Memory breakdown

Weights3.7 GB
KV Cache0.7 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsstablelm 2 zephyr 1 6b on RX 7600 XT 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: 45.6 tok/s decode · 4.2s TTFT (warm) · 114 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 well45.6 tok/s2314 ms224K
CodingCRuns well45.6 tok/s4242 ms224K
Agentic CodingCRuns well45.6 tok/s6170 ms224K
ReasoningCRuns well45.6 tok/s5013 ms224K
RAGCRuns well45.6 tok/s7713 ms224K

Inference speed

stablelm 2 zephyr 1 6b inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for stablelm 2 zephyr 1 6b at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~114 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M114.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M84.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M84.0Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M84.0Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M84.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M84.0Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M84.0Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M84.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M84.0Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M84.0Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M84.0Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M65.6Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M64.9Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M60.1Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M54.3Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M52.8Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How stablelm 2 zephyr 1 6b (6B params) fits at each quantization level on RX 7600 XT 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.3 GB
LowC46
Q3_K_S
3
2.9 GB
LowC47
NVFP4
4
3.4 GB
MediumC47
Q4_K_M
4
3.7 GB
MediumC48
Q5_K_M
5
4.3 GB
HighC48
Q6_K
6
4.9 GB
HighC49
Q8_0
8
6.4 GB
Very HighC50
F16Best for your GPU
16
12.3 GB
MaximumC51

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

Upgrade-Optionen

Hardware, die stablelm 2 zephyr 1 6b gut ausführt

RX 7900 XT 20GBGünstige Wahl
20 GB VRAM (+4)800 GB/s (+512)
C
Raises estimated decode speed by about 84%.84 tok/s Dekodierung

Raises estimated decode speed by about 84%.

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

ca. $899 MSRP

NVIDIARTX 4000 Ada 20GBBestes Preis-Leistungs-Verhältnis
20 GB VRAM (+4)360 GB/s (+72)
C
Raises estimated decode speed by about 68%.76.7 tok/s Dekodierung

Raises estimated decode speed by about 68%.

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

ca. $1,250 MSRP

Frequently asked questions

Can RX 7600 XT 16GB run stablelm 2 zephyr 1 6b?

Yes, RX 7600 XT 16GB can run stablelm 2 zephyr 1 6b with a C grade (Runs well). Expected decode speed: 45.6 tok/s.

How much VRAM does stablelm 2 zephyr 1 6b need?

stablelm 2 zephyr 1 6b (6B parameters) requires approximately 6.9 GB of memory with Q4_K_M quantization.

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

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

What speed will stablelm 2 zephyr 1 6b run at on RX 7600 XT 16GB?

On RX 7600 XT 16GB, stablelm 2 zephyr 1 6b achieves approximately 45.6 tokens per second decode speed with a time-to-first-token of 4242ms using Q4_K_M quantization.

Can RX 7600 XT 16GB run stablelm 2 zephyr 1 6b for coding?

For coding workloads, stablelm 2 zephyr 1 6b on RX 7600 XT 16GB receives a C grade with 45.6 tok/s and 224K context.

What context window can stablelm 2 zephyr 1 6b use on RX 7600 XT 16GB?

On RX 7600 XT 16GB, stablelm 2 zephyr 1 6b can safely use up to 224K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 7600 XT 16GBSee all hardware for stablelm 2 zephyr 1 6b
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-stabilityai--stablelm-2-zephyr-1-6b-on-rx-7600-xt-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: