MacBook Pro M3 24GBGünstige Wahl
24 GB Unified (+8)
C
This setup is broadly balanced for this model.28 tok/s Dekodierung
ca. $1,099 MSRP
Can Gemmasutra Mini 2B v1 run on RTX 4070 Ti Super 16GB?
YES — Runs Great
C44Usable
Estimated from fit model
Gemmasutra Mini 2B v1 needs ~4.0 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~28 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
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.
Select quantization to explore
Q4_K_M (Medium quality) — 4.0 GB, 32.0 tok/s, Runs well
4.0 GB required16.0 GB available
25% VRAM used
Fit status
Runs well
Decode
32.0 tok/s
TTFT
6050 ms
Safe context
838K
Memory
4.0 GB / 16.0 GB
Memory breakdown
Weights1.2 GB
KV Cache0.2 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsGemmasutra Mini 2B v1 on RTX 4070 Ti Super 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: 32.0 tok/s decode · 6.0s TTFT (warm) · 80 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
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 32.0 tok/s | 3300 ms | 838K |
| Coding | C | Runs well | 28.0 tok/s | 6914 ms | 838K |
| Agentic Coding | C | Runs well | 32.0 tok/s | 8800 ms | 838K |
| Reasoning | C | Runs well | 32.0 tok/s | 7150 ms | 838K |
| RAG | C | Runs well | 32.0 tok/s | 11000 ms | 838K |
Quantization options
How Gemmasutra Mini 2B v1 (2B params) fits at each quantization level on RTX 4070 Ti Super 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.8 GB | Low | C46 |
Q3_K_S | 3 | 1.0 GB | Low | C46 |
NVFP4 | 4 | 1.1 GB | Medium | C46 |
Q4_K_M | 4 | 1.2 GB | Medium | C46 |
Q5_K_M | 5 | 1.4 GB | High | C46 |
Q6_K | 6 | 1.6 GB | High | C46 |
Q8_0 | 8 | 2.1 GB | Very High | C46 |
F16Best for your GPU | 16 | 4.1 GB | Maximum | C48 |
Get started
Copy-paste commands to run Gemmasutra Mini 2B v1 on your machine.
Run
lms load hf-thedrummer--gemmasutra-mini-2b-v1-gguf && lms server startUpgrade-Optionen
Hardware, die Gemmasutra Mini 2B v1 gut ausführt
Frequently asked questions
Can RTX 4070 Ti Super 16GB run Gemmasutra Mini 2B v1?
Yes, RTX 4070 Ti Super 16GB can run Gemmasutra Mini 2B v1 with a C grade (Runs well). Expected decode speed: 28.0 tok/s.
How much VRAM does Gemmasutra Mini 2B v1 need?
Gemmasutra Mini 2B v1 (2B parameters) requires approximately 4.0 GB of memory with Q4_K_M quantization.
What is the best quantization for Gemmasutra Mini 2B v1?
The recommended quantization for Gemmasutra Mini 2B v1 is Q4_K_M, which balances quality and memory efficiency.
What speed will Gemmasutra Mini 2B v1 run at on RTX 4070 Ti Super 16GB?
On RTX 4070 Ti Super 16GB, Gemmasutra Mini 2B v1 achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6914ms using Q4_K_M quantization.
Can RTX 4070 Ti Super 16GB run Gemmasutra Mini 2B v1 for coding?
For coding workloads, Gemmasutra Mini 2B v1 on RTX 4070 Ti Super 16GB receives a C grade with 28.0 tok/s and 838K context.
What context window can Gemmasutra Mini 2B v1 use on RTX 4070 Ti Super 16GB?
On RTX 4070 Ti Super 16GB, Gemmasutra Mini 2B v1 can safely use up to 838K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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