Best GPU for Running LLMs Locally — Budget Guide 2026
Updated May 2026 · Real-world picks · Covers RTX 4060 to Mac Studio M4 Max
The right GPU for local LLM inference depends entirely on which models you want to run. An entry-level RTX 4060 is enough for 7B models at Q4 quantization. Running 70B without a multi-GPU setup requires Apple Silicon with 64 GB+ of unified memory. This guide covers every major budget tier and the exact models each card can handle.
Quick picks by model size
- 7B models — RTX 4060 8GB — cheapest option that works well
- 13B models at Q8 — RTX 4060 Ti 16GB — best budget pick; or RTX 5070 Ti 16GB for fastest 16GB Blackwell throughput
- 34B models — RTX 4090 24GB or RX 7900 XTX 24GB
- 70B models — Mac Studio M4 Max 64GB
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GPU Comparison Table
| GPU | VRAM | Price | Max model | Quantization | Best for | Buy |
|---|---|---|---|---|---|---|
| RTX 4060 8GB | 8 GB | See price | 7B | Q4_K_M | Entry local inference | Buy |
| RTX 4060 Ti 16GB | 16 GB | See price | 13B Q8 / 20B Q4 | Q8 | Best VRAM-per-dollar (NVIDIA) | Buy |
| RTX 4070 12GB | 12 GB | See price | 13B | Q4_K_M | Fast 7B–13B CUDA inference | Buy |
| RTX 5070 12GB | 12 GB | See price | 13B | Q4_K_M | Blackwell 12GB — faster than 4070 | Buy |
| RTX 5070 Ti 16GB | 16 GB | See price | 13B Q8 / 20B Q4 | Q8 | Best 16GB GDDR7 (Blackwell) | Buy |
| RTX 4070 Ti Super 16GB | 16 GB | See price | 13B Q8 / 20B Q4 | Q8 | 16GB CUDA sweet spot — fastest 13B Q8 | Buy |
| RX 7900 XTX 24GB | 24 GB | See price | 34B | Q4_K_M | Budget 24 GB VRAM (AMD) | Buy |
| RTX 3090 24GB | 24 GB | See price | 34B | Q4_K_M | Best used-market value | Buy |
| RTX 4080 16GB | 16 GB | See price | 13B Q8 / 20B Q4 | Q8 | Fast 13B–20B inference | Buy |
| RTX 4090 24GB | 24 GB | See price | 34B | Q4_K_M / 13B Q8 | Best single consumer GPU | Buy |
| RTX 5090 32GB | 32 GB | See price | 34B | Q8 | Top consumer GPU, future-proof | Buy |
| Mac Studio M4 Max 64GB | 64 GB | See price | 70B | Q4_K_M | Best for 70B models | Buy |
| Mac Studio M4 Max 128GB | 128 GB | See price | 70B | Q8 | 70B at near-lossless quality (macOS) | Buy |
| NVIDIA DGX Spark | 128 GB | See price | 200B | Q4_K_M | Best desktop NVIDIA 70B–200B inference | Buy |
"Max model" is the largest parameter count that fits in VRAM at the listed quantization with at least 1 GB headroom. Tokens/sec ranges (in the tier breakdown below) are cross-referenced with the XiongjieDai community llama-bench runs and the Home GPU LLM Leaderboard. Use the VRAM Calculator for exact figures. Compare any two options side-by-side on the Compare page.
Budget Tier Breakdown
Budget tier
Budget PickRTX 4060 8GB
Entry-level local inference, 7B chat & coding models
VRAM
8 GB
Max model
7B at Q4_K_M
Speed
20–30 t/s
Pros
- + Cheapest GPU for usable LLM inference
- + Low power draw (115 W TDP)
- + Fits Qwen3 8B, Llama 3.1 8B, DeepSeek-R1-Distill-8B at Q4_K_M
Cons
- - 8 GB VRAM — cannot run 13B+ models
- - No headroom for Q8 on 7B
The RTX 4060 is the go-to entry point for local LLMs in 2026. It runs any 7B model at Q4_K_M quantization with a comfortable 1–3 GB of headroom, delivering 20–30 tokens/second — fast enough for real-time chat. You cannot run 13B or larger models without slow CPU offloading, but a well-tuned 7B model handles most everyday tasks.
Value tier
Best Value 16GBRTX 4060 Ti 16GB
Best VRAM-per-dollar — 16 GB CUDA
VRAM
16 GB
Max model
20B at Q4_K_M / 13B at Q8
Speed
20–35 t/s
Pros
- + 16 GB VRAM — more than the pricier RTX 4070 12GB
- + Runs 13B models at Q8 (~14 GB) comfortably
- + Full CUDA support, low power draw (165 W TDP)
Cons
- - Slower than RTX 4070 Ti / 4090 at same model size
- - Limited by slower memory bus (256-bit vs 192-bit on 4070)
- - Cannot run 34B+ models
The RTX 4060 Ti 16GB is the hidden gem of the 2026 GPU market for LLM inference. It gives you 16 GB of GDDR6 VRAM — more than the pricier RTX 4070 12GB. This extra headroom lets you run 13B models at Q8 (near-lossless quality) or 20B models at Q4_K_M. It is slower than the 4070 due to a narrower memory bus, but VRAM wins for LLMs — you can run bigger models, not just faster ones.
Mid-range tier
Mid-Range Sweet SpotRTX 4070 12GB
Fast 7B at Q8, 13B at Q4, best CUDA support
VRAM
12 GB
Max model
13B at Q4_K_M
Speed
30–50 t/s
Pros
- + Excellent inference speed for the price
- + Full CUDA support — widest framework compatibility
- + Runs 13B at Q4_K_M with ~3 GB headroom
Cons
- - 12 GB limits you to 13B at Q4 — no 34B
- - Small step up in VRAM vs RTX 4060
RTX 4070 Ti Super 16GB
Best CUDA 16GB value — 2.3x faster than 4060 Ti at same VRAM
VRAM
16 GB
Max model
20B at Q4_K_M / 13B at Q8
Speed
50–100 t/s at 7B Q4
Pros
- + 672 GB/s bandwidth — 2.3x faster than RTX 4060 Ti at same VRAM
- + 16 GB GDDR6X runs 13B at Q8 and 20B at Q4_K_M
- + Noticeably faster tokens/s than 4060 Ti and 4070
Cons
- - More expensive than the 4060 Ti — paying for speed, not VRAM
- - Still limited to 16 GB — cannot run 34B+
- - 285 W TDP — higher power than 4060 Ti
RX 7900 XTX 24GB
34B models on a budget, large VRAM at low cost
VRAM
24 GB
Max model
34B at Q4_K_M
Speed
15–25 t/s
Pros
- + 24 GB VRAM — best VRAM-per-dollar
- + Handles 34B models at Q4_K_M
- + llama.cpp + Ollama ROCm support
Cons
- - ROCm software less mature than CUDA
- - Slower than NVIDIA at same VRAM size
- - Some tools require manual ROCm setup
The mid-range tier has three very different options. The RTX 4070 (12 GB) is the fast CUDA pick for 7B–13B models. The RTX 4070 Ti Super (16 GB) adds 4 GB more VRAM and 2.3x more bandwidth than the 4060 Ti — the sweet spot if you want 13B at Q8 with real speed. The RX 7900 XTX (24 GB) doubles VRAM to 24 GB for 34B model support, but AMD ROCm requires more setup.
Used-market tier
Best Used ValueRTX 3090 24GB (used)
Budget 24 GB VRAM build — same capacity as RTX 4090
VRAM
24 GB
Max model
34B at Q4_K_M
Speed
15–22 t/s
Pros
- + 24 GB VRAM at roughly one-third the price of a new RTX 4090
- + Full CUDA support — works with all frameworks
- + Can run same model sizes as RTX 4090
Cons
- - 25–30% slower inference than RTX 4090
- - Used market — no warranty, condition varies
- - Higher power draw (350 W TDP)
The RTX 3090 is the used-market standout for 2026. You get 24 GB of CUDA VRAM — the same as the RTX 4090 — on eBay or local resale markets. Inference speed is about 25–30% lower than the RTX 4090 due to the older memory architecture, but you can run the same models. If you want 24 GB VRAM without paying flagship prices, this is the best-value move.
Performance tier
Performance PickRTX 4080 16GB
Fast 13B–20B inference with headroom for long contexts
VRAM
16 GB
Max model
20B at Q4_K_M / 13B at Q8
Speed
35–55 t/s at 13B
Pros
- + 716 GB/s memory bandwidth — faster tokens/s than 4060 Ti at same VRAM
- + Same 16 GB as 4060 Ti but significantly faster inference
- + Full CUDA support, Ada Lovelace architecture
Cons
- - Same 16 GB VRAM ceiling as the RTX 4060 Ti
- - Much pricier than the 4060 Ti for ~1.5–2x the speed
- - RTX 5080 offers higher bandwidth at a higher price
The RTX 4080 is the speed pick in the 16 GB tier. You get the same 16 GB of VRAM as the RTX 4060 Ti but with 716 GB/s bandwidth — roughly 2.5x more throughput. That translates to noticeably faster tokens per second on 13B–20B models. If inference speed matters more than VRAM capacity, the 4080 is worth the premium over the 4060 Ti. For max VRAM on a budget, go with the 4060 Ti instead.
Flagship tier
Best Consumer GPURTX 4090 24GB
Best single-GPU performance for local LLMs
VRAM
24 GB
Max model
34B at Q4_K_M, 13B at Q8
Speed
25–40 t/s at 13B
Pros
- + Fastest consumer GPU for LLM inference
- + Full CUDA support across all frameworks
- + 24 GB handles 34B models at Q4_K_M comfortably
Cons
- - Premium price
- - Still cannot run 70B models without CPU offloading
- - 450 W TDP — high power draw
The RTX 4090 remains the best single consumer GPU for local LLMs in 2026. Its 24 GB GDDR6X runs 34B models at Q4_K_M and 13B models at Q8 with headroom to spare. Inference throughput is the fastest available in a consumer card — roughly twice as fast as the RTX 3090 on the same model. If you have the budget and want one card that handles everything up to 34B, this is it.
Top tier
Top of the LineRTX 5090 32GB
Maximum single-GPU performance, future-proofing
VRAM
32 GB
Max model
34B at Q8, 70B at Q4 (partial offload)
Speed
35–55 t/s at 13B
Pros
- + 32 GB GDDR7 — runs 34B at Q8 (near-lossless)
- + Fastest inference of any consumer GPU
- + Can run 70B at Q4 with modest CPU offloading
Cons
- - Most expensive consumer GPU
- - Costs more than the 4090 but is meaningfully more capable
- - 575 W TDP — requires high-end PSU
The RTX 5090 is the current top of the consumer GPU stack for LLMs. Its 32 GB of GDDR7 memory is the first consumer card to fit 34B models at Q8 quantization — near-lossless quality that was previously only possible on server hardware. For 70B models with partial CPU offloading, inference is workable at 5–10 tokens/second. It costs more than the RTX 4090 but is substantially more capable.
Workstation tier
Best for 70BMac Studio M4 Max (64GB)
70B models, silent operation, macOS ecosystem
VRAM
64 GB unified
Max model
70B at Q4_K_M, 34B at Q8
Speed
8–15 t/s at 70B
Pros
- + 64 GB unified memory — runs 70B at Q4_K_M with 27 GB headroom
- + Silent, low power (300 W max)
- + Excellent llama.cpp Metal performance
- + No driver issues — just works
Cons
- - Expensive for the GPU performance
- - Slower than RTX 4090 on 13B–34B models
- - Tied to macOS and Apple ecosystem
Mac Studio M4 Max (128GB)
Near-server-grade local inference, macOS ecosystem, 70B at Q8
VRAM
128 GB unified
Max model
70B at Q8, 34B at FP16
Speed
6–12 t/s at 70B Q8
Pros
- + 128 GB — fits 70B at Q8 (near-lossless quality)
- + Silent, energy efficient (300 W max)
- + Best option for macOS-native LLM tools (MLX framework)
Cons
- - Far more expensive than the NVIDIA DGX Spark for same VRAM
- - Slower AI compute than DGX Spark for inference workloads
- - Tied to macOS and Apple ecosystem
NVIDIA DGX Spark
70B–200B NVIDIA inference, CUDA/TensorRT-LLM stack
VRAM
128 GB unified (LPDDR5X)
Max model
70B at Q8, 200B at Q4
Speed
~8 t/s at 70B Q4, ~4 t/s at 70B Q8
Pros
- + 128 GB unified memory — far cheaper than the Mac Studio 128GB
- + 1 PFLOPS FP4 AI compute — purpose-built for LLM inference
- + Full CUDA support: TensorRT-LLM, llama.cpp CUDA, vLLM
- + Can dual-link two units for 256 GB to run 405B models
Cons
- - ~8 t/s at 70B is slower than RTX 4090 at 34B (memory bandwidth limited)
- - No GPU display output — pure compute device
- - New product: limited community benchmarks at launch
For 70B+ models, the two practical desktop options are the Mac Studio M4 Max and the NVIDIA DGX Spark. The Mac Studio M4 Max 64 GB runs 70B at Q4_K_M. The NVIDIA DGX Spark runs 70B at Q8 and 200B at Q4 — the most capable desktop AI workstation for NVIDIA users, at a lower price than the Mac Studio 128 GB. For CUDA/TensorRT-LLM workflows, the DGX Spark is the clear choice. For macOS-native tooling, the Mac Studio wins.
How to Choose the Right GPU
For most budgets, the RTX 4090 24GB is the best GPU for local LLMs in 2026 — it runs 34B models at Q4 and 13B at FP16. On a tighter budget, the RTX 4070 12GB handles 7B–13B models well. Apple Silicon M4 Pro/Max wins when you need 48GB+ at low power.
- 1. Decide which models you want to run. The model size — not inference speed — is the primary constraint. A 7B model needs ~5 GB VRAM at Q4. A 70B model needs ~37 GB. Use the VRAM Calculator to check any specific model.
- 2. Choose VRAM first, speed second. A faster GPU that cannot fit your model is useless. Prioritize having enough VRAM with at least 1–2 GB headroom for KV cache. Then consider inference throughput.
- 3. Prefer NVIDIA if software maturity matters. CUDA is better supported across Ollama, llama.cpp, LM Studio, vLLM, and most fine-tuning tools. AMD ROCm works for inference but requires more configuration and has less community troubleshooting content.
- 4. Consider the used market for 24 GB VRAM. A used RTX 3090 gives you the same 24 GB VRAM as the RTX 4090 at around 25–30% lower inference speed. If budget is tight and you can accept slower throughput, it is a legitimate option.
- 5. For 70B, go Apple Silicon or multi-GPU. No single consumer GPU card fits 70B at Q4_K_M. The Mac Studio M4 Max (64 GB) is the practical single-device option. Dual RTX 4090s (48 GB) work via llama.cpp tensor splitting but require a compatible motherboard and higher power draw.
Quantization and VRAM: Quick Reference
Every "max model" figure in this guide assumes Q4_K_M quantization unless noted. Quantization determines how many bits each model weight uses — and therefore how much VRAM the model occupies.
| Format | Bits/weight | Bytes/param | Quality loss | Typical use |
|---|---|---|---|---|
| Q4_K_M | 4-bit | ~0.5 | ~1–3% | Default for consumer GPUs — best VRAM efficiency |
| Q5_K_M | 5-bit | ~0.625 | ~0.5–1% | Slight quality improvement over Q4 with moderate VRAM cost |
| Q8_0 | 8-bit | ~1.0 | <0.1% | Near-lossless — use when VRAM allows |
| FP16 | 16-bit | 2.0 | Reference | Full precision — fine-tuning, research |
Use the VRAM Calculator to compute exact memory requirements for any model size and quantization combination.
Frequently Asked Questions
What is the best GPU for running LLMs locally in 2026?
The RTX 4090 (24 GB) is the best single consumer GPU for local LLM inference — fast, CUDA-native, and handles 34B models at Q4_K_M comfortably. For the best value, the RTX 4060 covers 7B models and the RTX 3090 covers 32B at a lower price. For 70B models, the Mac Studio M4 Max (64 GB) is the practical choice.
Can I run a good LLM on a budget GPU?
Yes. The RTX 4060 (8 GB) runs 7B models like Qwen3 8B, Llama 3.1 8B, and Gemma 3 4B at Q4_K_M quantization with smooth 20–30 tokens/second throughput. For most everyday chat and coding tasks, a well-tuned 7B model at Q4 is genuinely useful.
Is the RTX 3090 still worth buying used for LLMs?
Yes — the RTX 3090 offers 24 GB VRAM on the used market, matching the RTX 4090 in memory capacity for a fraction of the cost. Inference speed is roughly 20% lower. For a budget-conscious builder who wants to run 13B–34B models at Q4_K_M, it is the best used-market value in 2026.
Is a Mac better than a GPU PC for running LLMs?
For 7B–34B models, a GPU PC with an RTX 4090 is faster and cheaper. For 70B models, the Mac Studio M4 Max is the only practical single-device option. Macs also have zero driver overhead, near-silent operation, and excellent llama.cpp Metal support.
What is the difference between the RTX 4070 and RX 7900 XTX for LLMs?
The RTX 4070 (12 GB) is faster for 7B–13B models and has better software support via CUDA. The RX 7900 XTX (24 GB) doubles the VRAM, enabling 34B models at Q4_K_M — but AMD ROCm requires more setup. If you want maximum VRAM per dollar and are comfortable with AMD, the 7900 XTX wins. If you want the easiest setup, the RTX 4070 wins.
How much VRAM do I need for a 70B model?
A 70B model at Q4_K_M requires approximately 37–40 GB of VRAM. No single consumer GPU card has enough — you need a Mac Studio M4 Max with 64 GB unified memory, or two RTX 4090s (48 GB combined) using llama.cpp tensor splitting. The RTX 5090 (32 GB) can run 70B with partial CPU offloading at reduced speed.
Does the RTX 5090 support LLM inference?
Yes. The RTX 5090 (32 GB GDDR7) works with Ollama, llama.cpp, LM Studio, and all major inference frameworks. Its 32 GB fits 34B models at Q8 (near-lossless quality) and is the fastest single consumer GPU for LLM inference in 2026.
GPU-specific guides
Model hardware guides
Find hardware for a specific model or check exact VRAM requirements.
Related Guides
LLM RAM Requirements
How much RAM and VRAM you need for different model sizes.
AMD vs Nvidia for LLMs
Compare GPU vendors for local AI inference and fine-tuning.
Best Budget GPUs
Top budget-friendly GPUs that still handle LLMs well.
LLM Quantization Guide
Reduce VRAM usage with quantization to run larger models.
Sources & methodology
VRAM and tokens-per-second figures on this page are synthesised from open community benchmarks. The sitewide formula and the full source list are on the methodology page. For this guide specifically I leaned on:
- XiongjieDai GPU-Benchmarks-on-LLM-Inference — community llama-bench runs across 30+ consumer and workstation GPUs, source of the per-card tokens-per-second numbers.
- Home GPU LLM Leaderboard — ranked head-to-head leaderboard used to sanity-check the tier ordering at each VRAM bucket.
- Hardware Corner GPU ranking — independent local-LLM GPU ranking; used as a tiebreaker where the other two disagreed by more than ~10%.
- llama.cpp llama-bench discussion — the upstream benchmarking thread the community numbers all derive from.
Spot a number that does not match the linked source? Email billybobgurr@gmail.com and I will update the guide.