Will It Run AI

Can logos16v2 stablelm2 1.6b i1 run on Radeon RX 7900M 16GB?

YES — Runs Great

C42Usable
Estimated from fit model

logos16v2 stablelm2 1.6b i1 needs ~3.7 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 3.7 GB, 22.4 tok/s, Runs well
3.7 GB required16.0 GB available
23% VRAM used

Fit status

Runs well

Decode

22.4 tok/s

TTFT

8643 ms

Safe context

1.1M

Memory

3.7 GB / 16.0 GB

Memory breakdown

Weights1.0 GB
KV Cache0.2 GB
Runtime0.9 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelslogos16v2 stablelm2 1.6b i1 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: 22.4 tok/s decode · 8.6s TTFT (warm) · 56 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 well22.4 tok/s4714 ms1.0M
CodingCRuns well22.4 tok/s8643 ms1.1M
Agentic CodingCRuns well22.4 tok/s12571 ms1.1M
ReasoningCRuns well22.4 tok/s10214 ms1.1M
RAGCRuns well22.4 tok/s15714 ms1.1M

Quantization options

How logos16v2 stablelm2 1.6b i1 (1.600000023841858B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
0.6 GB
LowC45
Q3_K_S
3
0.8 GB
LowC45
NVFP4
4
0.9 GB
MediumC45
Q4_K_M
4
1.0 GB
MediumC45
Q5_K_M
5
1.2 GB
HighC45
Q6_K
6
1.3 GB
HighC45
Q8_0
8
1.7 GB
Very HighC46
F16Best for your GPU
16
3.3 GB
MaximumC47

Get started

Copy-paste commands to run logos16v2 stablelm2 1.6b i1 on your machine.

Run

lms load hf-mradermacher--logos16v2-stablelm2-1-6b-i1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien logos16v2 stablelm2 1.6b i1

MacBook Pro M4 32GBOpción económica
32 GB Unified (+16)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.22.4 tok/s decodificación

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

~$799 MSRP

MacBook Pro M3 24GBMejor relación calidad-precio
24 GB Unified (+8)
C
Este setup está bastante equilibrado para este modelo.22.4 tok/s decodificación

~$1,099 MSRP

Frequently asked questions

Can Radeon RX 7900M 16GB run logos16v2 stablelm2 1.6b i1?

Yes, Radeon RX 7900M 16GB can run logos16v2 stablelm2 1.6b i1 with a C grade (Runs well). Expected decode speed: 22.4 tok/s.

How much VRAM does logos16v2 stablelm2 1.6b i1 need?

logos16v2 stablelm2 1.6b i1 (1.600000023841858B parameters) requires approximately 3.7 GB of memory with Q4_K_M quantization.

What is the best quantization for logos16v2 stablelm2 1.6b i1?

The recommended quantization for logos16v2 stablelm2 1.6b i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will logos16v2 stablelm2 1.6b i1 run at on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, logos16v2 stablelm2 1.6b i1 achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8643ms using Q4_K_M quantization.

Can Radeon RX 7900M 16GB run logos16v2 stablelm2 1.6b i1 for coding?

For coding workloads, logos16v2 stablelm2 1.6b i1 on Radeon RX 7900M 16GB receives a C grade with 22.4 tok/s and 1.1M context.

What context window can logos16v2 stablelm2 1.6b i1 use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, logos16v2 stablelm2 1.6b i1 can safely use up to 1.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon RX 7900M 16GBSee all hardware for logos16v2 stablelm2 1.6b i1
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