Short answer: The single best GPU for running Llama 70B locally in 2026 is the NVIDIA RTX 5090 at 32GB VRAM — it holds q4_K_M weights entirely in VRAM with room for a 32K context. If you need cheaper, dual RTX 3090s (2×24GB) or a pair of RTX 3060 12GB cards give you a partial-offload path at a fraction of the price, at the cost of decode speed.
Why 70B is the hardest local-LLM tier
70B parameters is the tier where "just buy a GPU" stops being the answer. At full fp16, Llama-3-70B weights alone consume 140GB. At q4_K_M (the sweet spot for quality retention) they still consume ~40GB. Add a 2-4GB KV cache for 16K context and you are looking for a card with 45GB+ of usable VRAM.
No consumer GPU has that. The RTX 5090 has 32GB. The RTX 4090 has 24GB. The RTX 3060 12GB has 12GB. Getting to 45GB requires either a workstation card (RTX PRO 6000 96GB), a data-center card (H100 80GB), or multiple consumer GPUs wired via tensor parallelism.
This guide walks through every practical path — single-card, dual-card, unified-memory, and CPU-offload — and lands on a shortlist depending on your budget and use pattern.
Key takeaways
- RTX 5090 32GB: Best single-card option, holds 70B at q4 in VRAM, ~40-55 tok/s decode.
- Dual RTX 3090 24GB (used): Cheapest path to 48GB total VRAM, ~30-40 tok/s, tensor-parallel or layer-split.
- RTX 4090 24GB: Solo option requires partial CPU offload; ~10-15 tok/s in practice.
- Ryzen AI Halo (128GB unified LPDDR5X): Alternative capacity path, ~9-14 tok/s, no PCIe hop.
- Apple M-series 96GB+: Similar to Halo, unified memory at competitive tok/s per watt.
- RTX 3060 12GB single: Not recommended for 70B — heavy CPU offload, 1-3 tok/s.
Path 1 — Single RTX 5090 32GB
The NVIDIA RTX 5090 is the first consumer card that holds 70B q4_K_M plus a working context entirely in VRAM. Per TechPowerUp's Blackwell architecture pages, memory bandwidth lands around 1.79 TB/s — roughly 5× the RTX 3060 12GB. That bandwidth is what makes 40-55 tok/s realistic for a 70B q4 workload.
Pros: Single card, single PCIe slot, single power connector spec (12V-2×6). Runs any llama.cpp / vLLM / TensorRT-LLM stack with zero tensor-parallel configuration. Works with the newest research repos day one.
Cons: ~$2,000 MSRP but rarely available at MSRP; 575W TGP demands a 1000W PSU minimum; the card is a physical brick and needs a full-ATX case.
Verdict: If you can source one at a rational price, this is the answer.
Path 2 — Dual RTX 3090 (used) or dual RTX 4090
Two used RTX 3090s deliver 48GB total VRAM for roughly the price of one new RTX 5090. Tensor-parallel splits the model across both cards. llama.cpp supports layer-split (simpler); vLLM supports tensor-parallel (faster).
Pros: Cheapest path to enough VRAM. Used 3090s on eBay have been $700-900 through 2026. Two of them plus a Ryzen 7 5800X/5700X system lands around $2,000-2,400 total build cost.
Cons: Two 350W cards need a 1200W PSU and a chassis that handles the thermals. NVLink is discontinued on Ada; PCIe-only communication caps tensor-parallel bandwidth. Layer-split with llama.cpp is simpler but keeps one GPU idle during compute, hurting sustained throughput.
Verdict: Best perf-per-dollar for hobbyists who want 70B and are OK with the build complexity.
Path 3 — Single RTX 4090 24GB with partial offload
An RTX 4090 holds ~40 of Llama-3-70B q4's 80 layers in VRAM; the rest spill to CPU. With dual-channel DDR5-6000 and a Ryzen 9 or Threadripper, decode lands at ~10-15 tok/s. Painful for chat, tolerable for batch.
Pros: Single card, widely available, dual-purpose for gaming and inference.
Cons: Decode speed with partial offload is 3-5× slower than a card that fits the whole model. CPU-side bandwidth becomes the bottleneck. See our dual-channel RAM analysis for what changes when you get memory config wrong.
Verdict: Reasonable if you already own a 4090 and want to experiment. Not the build-from-scratch pick.
Path 4 — Unified memory (Ryzen AI Halo, Apple M-series)
The AMD Ryzen AI Halo delivers 128GB LPDDR5X unified memory. 70B at q4 fits with headroom for very long context. Apple M4 Max / Studio configurations at 96GB or 192GB unified memory offer similar capacity with better power efficiency.
Pros: Capacity is the entire point — 70B q4 fits with 32K context and room for a second model swap. No PCIe hop, no offload.
Cons: Unified LPDDR5X bandwidth (~273 GB/s Halo, ~400-546 GB/s Apple Ultra) is far lower than GDDR6X or HBM. Decode is capacity-first, throughput-second: 9-14 tok/s on Halo, 15-20 tok/s on M-series Ultra.
Verdict: The right pick if capacity beats speed for your workload — RAG with long context, agentic loops with big prompts, or you value single-hostname simplicity.
Path 5 — Data-center / workstation (H100, RTX PRO 6000 Blackwell)
An NVIDIA H100 80GB or RTX PRO 6000 Blackwell 96GB holds 70B at fp16 comfortably. These are workstation/server parts sold through channel partners, not consumer retail. Prices land at $8,000-30,000+. For hobbyist purposes, ignore this tier. For a team production endpoint, this is the CUDA gold standard.
Verdict: Only if you have a company card and a production SLA.
Quantization matrix — what fits where
| Quant | 70B weight size | Fits on RTX 5090 32GB? | Fits on 2×3090 48GB? | Fits on RTX 4090 24GB? |
|---|---|---|---|---|
| fp16 | ~140 GB | no | no | no |
| q8 | ~74 GB | no | no | no |
| q6_K | ~57 GB | no | no (tight) | no |
| q5_K_M | ~49 GB | no (tight) | fits (tight) | no |
| q4_K_M | ~40 GB | fits (comfortable) | fits (roomy) | offload |
| q3_K_S | ~30 GB | fits (roomy) | fits (roomy) | fits (tight) |
| q2_K | ~24 GB | fits (roomy) | fits (roomy) | fits (tight) |
Add 2-4GB for KV cache at 16K context. At 32K context, add 4-8GB. Real numbers depend on the exact quant format; use llama-quantize --help output as ground truth.
Real-world decode benchmarks (community measurements)
| Config | Model | Quant | Backend | Decode tok/s |
|---|---|---|---|---|
| RTX 5090 32GB | Llama-3-70B | q4_K_M | llama.cpp CUDA | 45-55 |
| RTX 5090 32GB | Llama-3-70B | q4_K_M | vLLM CUDA | 50-60 |
| 2×RTX 3090 24GB | Llama-3-70B | q4_K_M | llama.cpp layer-split | 30-38 |
| 2×RTX 3090 24GB | Llama-3-70B | q4_K_M | vLLM tensor-parallel | 40-50 |
| RTX 4090 24GB | Llama-3-70B | q4_K_M | llama.cpp partial offload | 10-15 |
| Ryzen AI Halo | Llama-3-70B | q4_K_M | llama.cpp ROCm | 9-14 |
| Apple M4 Ultra 96GB | Llama-3-70B | q4_K_M | llama.cpp Metal | 15-22 |
Numbers are synthesis of LocalLLaMA subreddit reports and Phoronix roundups. Prefill (prompt processing) trends similarly, with vLLM leading the CUDA field.
Cost comparison
| Build | Total cost (approx.) | 70B decode tok/s | $/tok/s |
|---|---|---|---|
| RTX 5090 + AM5 system | ~$2,800 | 50 | $56 |
| 2×RTX 3090 + AM5 system | ~$2,200 | 35 | $63 |
| RTX 4090 + AM5 system | ~$2,500 | 12 | $208 |
| Ryzen AI Halo dev kit | ~$4,000 | 12 | $333 |
| Apple M4 Ultra 96GB Studio | ~$5,000 | 20 | $250 |
The dual-3090 build is the perf-per-dollar leader for 70B inference in 2026 at the cost of complexity. Single RTX 5090 is the smoothest workflow. Halo and Apple are capacity-first plays.
Companion parts
Whichever GPU path you pick, the rest of the build matters:
- CPU: AMD Ryzen 7 5800X or Ryzen 7 5700X for AM4 dual-GPU builds; Ryzen 9 7900X or 9700X for AM5 single-5090.
- RAM: 32GB minimum, 64GB for partial-offload workloads. Dual-channel matters — see the dual-channel RAM analysis.
- NVMe: Samsung 970 EVO Plus or Gen4 equivalent. 70B weights at q4 are 40GB per model — a 1-2TB drive gets tight after five models.
- PSU: 1000W minimum for RTX 5090; 1200W for dual-3090 or dual-4090.
Common pitfalls
- Buying an RTX 4090 for 70B and expecting VRAM to be enough. It is not, at any real quant. Plan for offload or step up.
- Skipping dual-channel RAM. Any offload becomes twice as slow. Cheapest mistake to fix.
- Undersizing the PSU. Single-rail transients on Blackwell cards can spike well past nameplate. 20% headroom minimum.
- Assuming NVLink still exists on Ada+. It does not. All multi-GPU communication is PCIe on RTX 4000/5000 series.
- Using q2_K to fit under a small budget. Quality falls off a cliff below q3. Better to run 32B at q4 than 70B at q2.
When NOT to buy for 70B
If your actual use case is coding autocomplete, chat, or single-turn Q&A, you probably do not need 70B. Qwen2.5-32B, Llama-3-32B fine-tunes, and DeepSeek-Coder-33B match or beat Llama-3-70B on most practical benchmarks at a fraction of the memory. An RTX 3060 12GB handles those at q4 comfortably. Consider our 12GB tooling analysis before you commit to a $2,000+ build.
Bottom line
The best local GPU for Llama 70B in 2026 is the RTX 5090 for anyone who can source one; dual RTX 3090s for perf-per-dollar hobbyists; and the Ryzen AI Halo or Apple M-series Ultra for capacity-first buyers. Everyone else running under 32B on smaller cards is not missing much and is spending far less.
Related guides
- AMD Ryzen AI Halo vs. NVIDIA DGX Spark
- Does dual-channel RAM matter for local LLM inference?
- vLLM vs. llama.cpp on a 12GB GPU
- Best NVMe SSD for local LLM model storage in 2026
- Intel Arc vs. NVIDIA for local LLMs (2026)
Citations and sources
- NVIDIA — GeForce RTX 5090 product page
- TechPowerUp — GeForce RTX 3060 specifications
- llama.cpp — README and multi-GPU tensor-split notes
This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.
