Question 1

What models can I run with 48.0 GB VRAM?

Accepted Answer

With 48.0 GB VRAM, you can run 1605 LLM models at various quantization levels. Popular models that fit well include Mixtral 8x7B Instruct v0.1, Mixtral 8x7B v0.1, Falcon 40B. 1113 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.

Question 2

Is 48.0 GB enough for local AI?

Accepted Answer

48.0 GB is excellent for local AI. You have access to 1605 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.

Question 3

What GPU should I get for 48.0 GB VRAM?

Accepted Answer

Popular GPUs with ~48.0 GB include NVIDIA A40, AMD Radeon PRO W7900, 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.

Question 4

How to choose the right model size for 48.0 GB?

Accepted Answer

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.

Question 5

Is 48.0 GB worth it over 24.0 GB?

Accepted Answer

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.

Model	Quant	VRAM	Speed	Context	Status	Grade
Mixtral 8x7B Instruct v0.146.7BChat Q4_K_M·21.8 t/s tok/s·33K ctx·DECENT	Q4_K_M	28.6 GB60%	21.8 t/s	33K	DECENT	B62
Mixtral 8x7B v0.146.7BChat Q4_K_M·21.8 t/s tok/s·33K ctx·DECENT	Q4_K_M	28.6 GB60%	21.8 t/s	33K	DECENT	B62
Falcon 40B41.8BChat Q4_K_M·22.6 t/s tok/s·DECENT	Q4_K_M	27.6 GB58%	22.6 t/s	—	DECENT	B63
Phi 3.5 MoE Instruct41.9BChatCode Q4_K_M·24.3 t/s tok/s·131K ctx·RUNS WELL	Q4_K_M	25.7 GB54%	24.3 t/s	131K	RUNS WELL	A65
Qwen3.6 35B A3B36.0BVision Q4_K_M·28.4 t/s tok/s·262K ctx·RUNS WELL	Q4_K_M	21.9 GB46%	28.4 t/s	262K	RUNS WELL	A69
Gemma 4 31B IT32.7BVision Q4_K_M·29.4 t/s tok/s·262K ctx·RUNS WELL	Q4_K_M	21.2 GB44%	29.4 t/s	262K	RUNS WELL	A70
Qwen3 32B32.8BChat Q4_K_M·30.8 t/s tok/s·41K ctx·RUNS WELL	Q4_K_M	20.3 GB42%	30.8 t/s	41K	RUNS WELL	A71
Ornith 1.0 35B35.1BChat Q4_K_M·29.1 t/s tok/s·262K ctx·RUNS WELL	Q4_K_M	21.4 GB45%	29.1 t/s	262K	RUNS WELL	A70
Falcon 40B Instruct40BChat Q4_K_M·23.6 t/s tok/s·DECENT	Q4_K_M	26.4 GB55%	23.6 t/s	—	DECENT	B64
Qwen2.5 Coder 32B Instruct32.8BChatCode Q4_K_M·30.4 t/s tok/s·33K ctx·RUNS WELL	Q4_K_M	20.5 GB43%	30.4 t/s	33K	RUNS WELL	A71
DeepSeek R1 Distill Qwen 32B32.8BChatReasoning Q4_K_M·30.4 t/s tok/s·131K ctx·RUNS WELL	Q4_K_M	20.5 GB43%	30.4 t/s	131K	RUNS WELL	A71
Gemma 4 26B A4B IT26.5BVision Q4_K_M·37.7 t/s tok/s·262K ctx·RUNS WELL	Q4_K_M	16.6 GB35%	37.7 t/s	262K	RUNS WELL	A74
QwQ 32B32.8BChatReasoning Q4_K_M·30.4 t/s tok/s·41K ctx·RUNS WELL	Q4_K_M	20.5 GB43%	30.4 t/s	41K	RUNS WELL	A71
Qwen3 30B A3B Instruct 250730.5BChat Q4_K_M·33.3 t/s tok/s·262K ctx·RUNS WELL	Q4_K_M	18.7 GB39%	33.3 t/s	262K	RUNS WELL	A72
Qwen3.6 27B27.8BVision Q4_K_M·35.8 t/s tok/s·262K ctx·RUNS WELL	Q4_K_M	17.4 GB36%	35.8 t/s	262K	RUNS WELL	A73
Qwen3 Coder 30B A3B Instruct30.5BChatCode Q4_K_M·33.3 t/s tok/s·262K ctx·RUNS WELL	Q4_K_M	18.7 GB39%	33.3 t/s	262K	RUNS WELL	A72

Best LLMs for 48 GB VRAM

Runs Well

Challenging

GPUs with ~48.0 GB VRAM

NVIDIA A40

AMD Radeon PRO W7900

NVIDIA L40

NVIDIA L40S

NVIDIA Quadro RTX 8000

NVIDIA RTX A6000

Models That Fit in 48 GB VRAM

Frequently Asked Questions