llmrun

Best LLMs for 48 GB VRAM

Professional / Apple Silicon (RTX 6000 Ada, L40S, MacBook Pro M4 Max 48GB) — 70B at Q4-Q5

← 24 GB VRAM

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

All 8 GPUs

AMD Radeon PRO W7900

AMD · RDNA 3

48 GB
864.0 GB/s6,144 SP295W TDP$3,999

NVIDIA RTX A6000

NVIDIA · Ampere

48 GB
768.0 GB/s10,752 CUDA300W TDP$4,649

Intel Arc Pro B60 Dual 48GB

Intel · Xe2 (Battlemage), 2x BMG-G21 (Xe2-HPG)

48 GB
456.0 GB/s400W TDP$1,200

NVIDIA Quadro RTX 8000

NVIDIA · Turing

48 GB
672.0 GB/s4,608 CUDA260W TDP$9,999

NVIDIA L40S

NVIDIA · Ada Lovelace

48 GB
864.0 GB/s18,176 CUDA350W TDP

NVIDIA L40

NVIDIA · Ada Lovelace

48 GB
864.0 GB/s18,176 CUDA300W TDP

Models That Fit in 48 GB VRAM

Speed estimated for NVIDIA RTX 6000 Ada Generation

113 models · 5 good

LLM models ranked by compatibility and performance
ModelVRAMGrade
Mixtral 8x7B Instruct v0.146.7B
Q4_K_M·21.8 t/s tok/s·33K ctx·GOOD FIT
28.6 GBA76
Mixtral 8x7B v0.146.7B
Q4_K_M·21.8 t/s tok/s·33K ctx·GOOD FIT
28.6 GBA76
Falcon 40B41.8B
Q4_K_M·22.6 t/s tok/s·GOOD FIT
27.6 GBA74
Phi 3.5 MoE Instruct41.9B
Q4_K_M·24.3 t/s tok/s·131K ctx·GOOD FIT
25.7 GBA69
Qwen3.6 35B A3B36.0B
Q4_K_M·28.4 t/s tok/s·262K ctx·FAIR FIT
21.9 GBB61
Gemma 4 31B IT32.7B
Q4_K_M·29.4 t/s tok/s·262K ctx·FAIR FIT
21.2 GBB59
Falcon 40B Instruct40B
Q4_K_M·23.6 t/s tok/s·GOOD FIT
26.4 GBA70
Qwen2.5 Coder 32B Instruct32.8B
Q4_K_M·30.4 t/s tok/s·33K ctx·FAIR FIT
20.5 GBB58
Qwen3 32B32.8B
Q4_K_M·30.8 t/s tok/s·41K ctx·FAIR FIT
20.3 GBB57
Gemma 4 26B A4B IT26.5B
Q4_K_M·37.7 t/s tok/s·262K ctx·FAIR FIT
16.6 GBB50
DeepSeek R1 Distill Qwen 32B32.8B
Q4_K_M·30.4 t/s tok/s·131K ctx·FAIR FIT
20.5 GBB58
Qwen3 Coder 30B A3B Instruct30.5B
Q4_K_M·33.3 t/s tok/s·262K ctx·FAIR FIT
18.7 GBB54
Qwen3.6 27B27.8B
Q4_K_M·35.8 t/s tok/s·262K ctx·FAIR FIT
17.4 GBB51
C4ai Command R V0135.0B
Q4_K_M·27.0 t/s tok/s·FAIR FIT
23.1 GBB63
Gemma 3 27B IT27.4B
Q4_K_M·34.5 t/s tok/s·131K ctx·FAIR FIT
18.1 GBB53
Qwen3 30B A3B Instruct 250730.5B
Q4_K_M·33.3 t/s tok/s·262K ctx·FAIR FIT
18.7 GBB54

Frequently Asked Questions

What models can I run with 48.0 GB VRAM?

With 48.0 GB VRAM, you can run 1337 LLM models at various quantization levels. Popular models that fit well include Mixtral 8x7B Instruct v0.1, Mixtral 8x7B v0.1, Falcon 40B. 11 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 1337 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 RTX A6000, Intel Arc Pro B60 Dual 48GB. 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/s
864 GB/s
864 GB/s
864 GB/s
768 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.