Best GPU for Llama models
Honest 2026 guide to picking a GPU for Llama 3.3 70B, Llama 4 Scout (109B/17B MoE), Llama 4 Maverick. Real picks per size + quant + context budget.
The short answer
For Llama 3.3 70B Q4 (the dominant local-AI workload in 2026), 24 GB VRAM is the sweet spot: used RTX 3090 at $800 or RTX 4090 at $1,800.
For Llama 3.1 8B daily, any 12+ GB card works fine. RTX 4060 Ti 16 GB at $450 is the value floor.
For Llama 4 Scout (109B/17B MoE) and Maverick (400B+ MoE), the MoE math kicks in: total weights are huge but active params are smaller. Mac Studio's unified memory or quad-GPU clusters are the local paths.
The picks, ranked by buyer-leverage
16 GB · $450-550 (2026 retail)
Cheapest CUDA path for Llama 3.1 8B + 13B Q4 daily inference.
- Llama 3.1 8B chat assistants
- Llama 3.1 13B-class workflows
- 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)
The single highest-leverage Llama 3.3 70B Q4 pick. 24 GB at half the cost of new alternatives.
- Llama 3.3 70B Q4 daily inference
- Multi-GPU homelab targeting Llama 4 Scout/Maverick
- Cost-conscious Llama experimentation
- Buyers who hate used silicon
- FP16 70B inference (need 48 GB+)
- Sustained 24/7 production (Ada more efficient)
32 GB · $2,000-2,500 (2026 retail)
32 GB unlocks Llama 3.3 70B Q4 at 32K+ context. Llama 4 Scout (17B active MoE) fits comfortably.
- Llama 3.3 70B production with long context
- Llama 4 Scout 109B/17B MoE inference
- FP8 native support for newer Llama variants
- Buyers running only Llama 8B / 13B (4060 Ti is enough)
- 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)
The local path for Llama 4 Maverick (400B+ MoE). Unified memory holds the full model.
- Llama 4 Maverick daily inference
- Operators avoiding multi-GPU complexity
- Silent always-on Llama serving
- CUDA-locked workflows (vLLM serious, TensorRT)
- Llama 3.3 70B-only operators (4090 is plenty)
- $/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
Llama spans dense (3.1 8B, 3.3 70B) and MoE (Llama 4 Scout, Maverick). Dense models follow standard VRAM math. MoE models need total-weights room but throughput tracks active params.
- 12 GB — Llama 3.1 8B Q4 only. Below modern minimum for serious work.
- 16 GB — Llama 3.1 8B comfortable; 13B-class Q4 fits; 70B blocks you.
- 24 GB (Llama 3.3 70B sweet spot) — 70B Q4 with 4-8K context. The dominant 2026 Llama tier.
- 32 GB — 70B Q4 at 32K+ context. Llama 4 Scout active params fit.
- 192-512 GB unified (Mac Studio) — Llama 4 Maverick full MoE resident.
Compare these picks head-to-head
Frequently asked questions
What VRAM do I need for Llama 3.3 70B?
Q4 quantized: 24 GB minimum, comfortable. FP16: 140+ GB (unrealistic on consumer hardware — needs Mac Studio 192+ GB or 6× consumer GPUs). Most operators run Q4; quality is excellent on Llama architecture.
Llama vs Qwen vs DeepSeek — does the GPU choice differ?
Same VRAM tier for similar sizes (Llama 70B Q4 ≈ Qwen 72B Q4 ≈ DeepSeek R1 32B at Q5). The hardware decision is sizeand quant-driven, not family-driven. Pick based on the largest model you'll actually run.
Can I run Llama 4 Maverick at home?
Only on Mac Studio M3 Ultra 384+ GB unified, or a workstation cluster (4× H100 / 8× consumer GPUs). The 400B+ MoE total weights are workstation-class. Most operators stop at Llama 4 Scout (109B/17B) which fits more cleanly.
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