MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+12)410 GB/s (+110)
B
Raises estimated decode speed by about 44%.65.9 tok/s decode
Raises estimated decode speed by about 44%.
~$2,499 MSRP
Can Vicuna 7B run on NVIDIA L4 24GB?
YES — Runs Great
C54Usable
○Estimated from fit model
Vicuna 7B needs ~15.7 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~46 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 15.7 GB, 45.7 tok/s, Runs well
15.7 GB required24.0 GB available
65% VRAM used
Fit status
Runs well
Decode
45.7 tok/s
TTFT
4239 ms
Safe context
4K
Memory
15.7 GB / 24.0 GB
Memory breakdown
Weights4.3 GB
KV Cache7.8 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsVicuna 7B on NVIDIA L4 24GB
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: 45.7 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
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 45.7 tok/s | 2312 ms | 4K |
| Coding | C | Runs well | 45.7 tok/s | 4239 ms | 4K |
| Agentic Coding | C | Runs with offload | 45.7 tok/s | 6166 ms | 4K |
| Reasoning | C | Runs well | 45.7 tok/s | 5010 ms | 4K |
| RAG | C | Runs with offload | 45.7 tok/s | 7708 ms | 4K |
Quantization options
How Vicuna 7B (7B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C45 |
Q3_K_S | 3 | 3.4 GB | Low | C45 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Vicuna 7B on your machine.
Run
ollama run vicunaUpgrade options