Best GPU for Llama models
Choose hardware for Llama by total weight size, quantization, context and runtime. Compare single-GPU, offload and larger-memory configurations.
The short answer
Llama 3.3 70B Q4_K_M needs about 42 to 43 GB for weights, before KV cache and runtime. One 24 GB RTX 3090/4090 or 32 GB RTX 5090 requires weight offload. Two 24 GB cards can be viable with a supported split and a checked context budget.
For Llama 3.1 8B, a 12 to 16 GB GPU is a useful starting point with Q4 weights. Check the artifact, context and runtime allocation before buying. The prices below are historical reference ranges, not current merchant quotes.
Llama 4 Scout and Maverick require memory for all experts. Active parameter count does not describe their stored weight size. Check the full artifact against available GPU or unified memory, including space reserved for the operating system.
The picks, ranked by buyer-leverage
16 GB · $450-550 (2026 retail)
A 16 GB CUDA option for Llama 3.1 8B at a checked quantization and context.
- Llama 3.1 8B chat assistants
- Quantized 8B models with room for context
- First-time buyers wanting CUDA + warranty
- Llama 3.3 70B operators (16 GB blocks you)
- Long-context (32K+) agent loops
- Concurrent Llama + image gen on same GPU
24 GB · $700-1,000 (2026 used)
A single card has 24 GB. For 70B Q4, plan for weight offload or a supported two-card split.
- Smaller quantized Llama models on one card
- Two-card 70B Q4 experiments with a checked allocation
- Cost-conscious Llama experimentation
- Buyers who hate used silicon
- FP16 70B inference (about 140 GB of weights before overhead)
- Sustained 24/7 production (Ada more efficient)
32 GB · $2,000-2,500 (2026 retail)
32 GB provides more headroom than a 24 GB card. It cannot hold 70B Q4 or all Scout weights without offload.
- Quantized workloads with a measured need for more than 24 GB
- Offload experiments with adequate system RAM
- FP8 native support for newer Llama variants
- Buyers whose tested 8B workload fits a smaller card
- Multi-GPU operators (dual 3090 cheaper for 48 GB)
- Llama 4 Maverick operators (still need workstation tier)
192 GB · $5,000-9,500 (96-512 GB unified)
Choose the exact memory configuration for the full artifact. 192 GB does not hold 400B+ Q4 weights, even before overhead.
- Models that fit the selected unified-memory configuration
- Operators avoiding multi-GPU complexity
- Silent always-on Llama serving
- CUDA-locked workflows (vLLM serious, TensorRT)
- Buyers whose workload fits a smaller memory configuration
- $/perf-conscious buyers
HonestyWhy benchmark numbers on this page might not reflect your real experience
- tok/s is not user experience. Humans read at ~10-15 tok/s — anything above that is buffer time, not perceived speed.
- Context length changes everything. A 70B Q4 model at 1024 tokens generates ~25 tok/s; the same model at 32K context drops to ~8-12 tok/s as KV cache fills.
- Quantization changes the conclusion. Q4_K_M vs Q5_K_M vs Q8 produce different speed AND different quality. A benchmark at one quant doesn't translate to another.
- Thermal throttling changes long sessions. The first 15 minutes of a benchmark see boost-clock peak; the next 4 hours see steady-state, which is 5-15% slower depending on case airflow.
- Driver and runtime versions silently shift winners. A 2024 benchmark on PyTorch 2.4 + CUDA 12.4 doesn't reflect 2026 reality on PyTorch 2.6 + CUDA 12.6. Discount benchmarks older than 6 months.
- Vendor and YouTuber benchmarks are cherry-picked. The standard 'Llama 3.1 70B Q4 at 1024 tokens' chart shows peak decode on a tiny prompt — exactly the conditions least representative of daily use.
- Our ranking is by workload fit at the buyer's actual budget — not by raw benchmark order. A faster card that doesn't fit your workload ranks below a slower card that does.
We try to surface these caveats where they apply. If a number on this page reads more confident than it should, please email us via contact. See also our methodology and editorial philosophy.
How to think about VRAM tiers
Budget for total weights, KV cache and runtime. These are planning estimates, not measured context limits. Multi-GPU memory requires a supported split; unified memory is shared with the operating system.
- 12 GB — Start with Llama 3.1 8B Q4 and check the context budget.
- 16 GB — More headroom for Llama 3.1 8B Q4; 70B Q4 needs offload.
- 24 GB — 70B Q4 weights exceed one card. Use a smaller model, weight offload or a supported multi-GPU split.
- 32 GB — 70B Q4 still exceeds one card. Scout also needs capacity for all expert weights.
- 192 GB and larger unified-memory configurations — 70B FP16 needs about 140 GB of weights before overhead. For larger MoE models, check the full artifact against memory remaining after the OS and runtime.
Compare these picks head-to-head
Frequently asked questions
What VRAM do I need for Llama 3.3 70B?
Q4_K_M weights are about 42 to 43 GB before KV cache and runtime. A 24 GB or 32 GB card needs weight offload. Two 24 GB cards can work with a supported split and limited context headroom. FP16 weights are about 140 GB before overhead.
Llama vs Qwen vs DeepSeek — does the GPU choice differ?
Compare total parameters, the actual quantized artifact and the model's cache architecture. A 32B Q5 model has a different weight budget from a 70B Q4 model. Runtime support, context and concurrency also affect the choice.
Can I run Llama 4 Maverick at home?
It requires a large-memory system and a compatible runtime. Even raw 4-bit weights for 400B parameters exceed 200 GB, before quantization metadata, KV cache and runtime. Check the exact artifact and per-device allocation; a 192 GB configuration is insufficient for that Q4 working set.
Go deeper
- Best GPU for local AI (pillar) — All picks ranked across model families
- Best GPU for Qwen — Same hardware tier as Llama for similar sizes
- Best GPU for DeepSeek — Other top open model family
- Llama family — All variants + capability deep-dive
When it doesn't work
Hardware bought, set up correctly, still failing? The highest-volume local-AI errors and their fixes:
Common alternatives readers consider:
- If your budget is tighter →best budget GPU for local AI
- If you'd rather buy used →best used GPU for local AI
- If you're on Apple Silicon →best Mac for local AI
- If you're not sure what fits your build →the will-it-run checker
- If you don't want to buy anything yet →our editorial philosophy