Best LLMs for 48 GB VRAM
Professional / Apple Silicon (RTX 6000 Ada, L40S, MacBook Pro M4 Max 48GB) — 70B at Q4-Q5
With 48 GB of memory, this is a high-end configuration for local AI. You can comfortably run most open-source LLMs including large 70B parameter models at good quantization levels, making it one of the best setups for serious local AI work.
At this memory tier, nearly every popular open-source model is within reach. You can run Llama 3 70B at Q4_K_M or even Q5_K_M quantization with room to spare, handle coding assistants like DeepSeek Coder 33B at high quality, and easily run any 7B–30B model at full or near-full precision. Context windows remain generous even with larger models, so multi-turn conversations and long-document processing work smoothly.
Runs Well
- 70B models (Llama 3 70B, Qwen 72B) at Q4–Q5
- 30B models at Q6–Q8 quality
- 7B–14B models at full FP16 precision
- Vision models (LLaVA, CogVLM) without compromise
Challenging
- Mixture-of-experts models like Mixtral 8x22B at higher quants
- 120B+ models still require lower quantizations
GPUs with ~48.0 GB VRAM
AMD Radeon PRO W7900
AMD · RDNA 3
NVIDIA L40S
NVIDIA · Ada Lovelace
NVIDIA L40
NVIDIA · Ada Lovelace
NVIDIA A40
NVIDIA · Ampere
NVIDIA RTX 6000 Ada Generation
NVIDIA · Ada Lovelace
NVIDIA RTX A6000
NVIDIA · Ampere
Models That Fit in 48 GB VRAM
Speed estimated for NVIDIA RTX 6000 Ada Generation
32 models · 1 good
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
Q4_K_M·116.2 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.4 GB | 116.2 t/s | 131K | EASY RUN | C31 |
Q4_K_M·215.9 t/s tok/s·41K ctx·EASY RUN | Q4_K_M | 2.9 GB | 215.9 t/s | 41K | EASY RUN | D28 |
Q4_K_M·115.8 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.4 GB | 115.8 t/s | 131K | EASY RUN | C31 |
Q8_0·127.1 t/s tok/s·4K ctx·EASY RUN | Q8_0 | 4.9 GB | 127.1 t/s | 4K | EASY RUN | C30 |
Q4_K_M·102.3 t/s tok/s·8K ctx·EASY RUN | Q4_K_M | 6.1 GB | 102.3 t/s | 8K | EASY RUN | C32 |
Q4_K_M·125.1 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 5.0 GB | 125.1 t/s | 131K | EASY RUN | C30 |
Q4_K_M·315.2 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.0 GB | 315.2 t/s | 131K | EASY RUN | D27 |
Q4_K_M·236.4 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 2.6 GB | 236.4 t/s | 2K | EASY RUN | D28 |
Q4_K_M·945.5 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 0.7 GB | 945.5 t/s | 131K | EASY RUN | D26 |
Q4_K_M·945.5 t/s tok/s·33K ctx·EASY RUN | Q4_K_M | 0.7 GB | 945.5 t/s | 33K | EASY RUN | D26 |
Q4_K_M·617.8 t/s tok/s·2K ctx·EASY RUN | Q4_K_M | 1.0 GB | 617.8 t/s | 2K | EASY RUN | D26 |
Q4_K_M·218.9 t/s tok/s·131K ctx·EASY RUN | Q4_K_M | 2.9 GB | 218.9 t/s | 131K | EASY RUN | D28 |
Q4_K_M·472.7 t/s tok/s·8K ctx·EASY RUN | Q4_K_M | 1.3 GB | 472.7 t/s | 8K | EASY RUN | D27 |
Q4_K_M·14.0 t/s tok/s·33K ctx·POOR FIT | Q4_K_M | 44.6 GB | 14.0 t/s | 33K | POOR FIT | C40 |
Q4_K_M·13.5 t/s tok/s·131K ctx·POOR FIT | Q4_K_M | 46.2 GB | 13.5 t/s | 131K | POOR FIT | D29 |
Q4_K_M·13.4 t/s tok/s·131K ctx·POOR FIT | Q4_K_M | 46.6 GB | 13.4 t/s | 131K | POOR FIT | D25 |
Frequently Asked Questions
- What models can I run with 48.0 GB VRAM?
With 48.0 GB VRAM, you can run 1224 LLM models at various quantization levels. Popular models that fit well include Mixtral 8x7B Instruct v0.1, Qwen3 32B, DeepSeek R1 Distill Qwen 32B. 12 models achieve excellent performance at this VRAM level. At this tier, you have the flexibility to choose higher quantizations (Q5/Q6) for better quality on smaller models, or run larger models at Q4.
- Is 48.0 GB enough for local AI?
48.0 GB is excellent for local AI. You have access to 1224 compatible models, from small 7B assistants to large 30B+ parameter models. This is the enthusiast tier where most popular open-source LLMs work well out of the box. You can run coding assistants, chat models, and reasoning models without worrying about VRAM limits.
- What GPU should I get for 48.0 GB VRAM?
Popular GPUs with ~48.0 GB include AMD Radeon PRO W7900, NVIDIA L40S, NVIDIA L40. The NVIDIA RTX 6000 Ada Generation leads in memory bandwidth at 960.0 GB/s, which translates directly to faster token generation. When choosing a GPU for AI, memory bandwidth matters as much as VRAM capacity — it determines how fast the model can generate text. A newer GPU with the same VRAM but higher bandwidth will produce tokens significantly faster.
Higher memory bandwidth = faster token generation. All these GPUs have approximately 48 GB VRAM, but speed varies significantly by bandwidth.
Memory bandwidth comparison
960 GB/s864 GB/s864 GB/s864 GB/s768 GB/s- How to choose the right model size for 48.0 GB?
The key rule: your model must fit in VRAM including KV cache overhead. With 48.0 GB, here's a practical guide: 7B models at Q6–Q8 give you the best quality output. 14B models at Q4–Q5 offer a great quality/size balance. 30B+ models fit at Q4 but leave less room for context. Start with a 7B model at high quality and scale up as needed.
- Is 48.0 GB worth it over 24.0 GB?
Yes — the jump from 24.0 GB to 48.0 GB is meaningful for AI. You gain access to higher quantizations and larger parameter models that won't fit in 24 GB.