The RTX 5070 costs about one-third the price of an RTX 4090 and delivers competitive token speeds on small models. For local LLM inference, that sounds compelling — until you look at the VRAM. 12GB versus 24GB is not a minor spec difference. It is a hard wall that determines which models you can run at all.
NVIDIA GeForce RTX 4090
24GB GDDR6X24GB VRAM handles 13B at any quantization and 34B at Q3. The right choice for serious local LLM inference.
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Quick answer
RTX 4090 wins for LLM inference. VRAM capacity is the primary constraint in local LLM, and 24GB versus 12GB is a fundamental capability difference. The 5070 only makes sense if you exclusively run small models (7B and below) and want modern architecture at a lower price point.
Spec comparison
| Spec | RTX 5070 | RTX 4090 |
|---|---|---|
| VRAM | 12GB GDDR7 | 24GB GDDR6X |
| Memory bandwidth | ~672 GB/s | 1,008 GB/s |
| Architecture | Blackwell (2025) | Ada Lovelace (2022) |
| CUDA cores | 6,144 | 16,384 |
| TDP | 250W | 450W |
| Price (2026) | ~$875 | ~$2,200 |
| VRAM per dollar | 13.7 MB/$ | 10.9 MB/$ |
The 5070 wins on VRAM per dollar and power efficiency. The 4090 wins on absolute VRAM capacity, bandwidth, and raw compute.
VRAM capacity memory bandwidth Specs are manufacturer figures. Bar lengths are scaled independently per metric.
The hard VRAM ceiling
Here is the model list the RTX 5070 cannot run at all:
- Any 13B model at Q6+: Requires 12–14GB — exceeds 12GB with context
- Any 13B model at Q8: Requires 14GB — will not fit
- 13B at FP16: Requires 28GB — impossible
- Any 20B+ model: Requires 14GB+ at minimum quantization
- 34B models: Require 20GB+ at Q4 — impossible
- 70B models: Single GPU impossible at any quantization
The RTX 4090’s 24GB handles all 13B models at any quantization, 34B at Q3–Q4, and has meaningful headroom for context.
What the 5070 actually runs well
On models that fit in 12GB, the RTX 5070 is genuinely capable:
| Model | RTX 5070 tok/s | RTX 4090 tok/s | Notes |
|---|---|---|---|
| Llama 3 8B (Q4_K_M) | ~63 tok/s | ~95 tok/s | 4090 ~50% faster |
| Mistral 7B (Q4_K_M) | ~67 tok/s | ~100 tok/s | 4090 ~50% faster |
| Phi-4 14B (Q4_K_M) | ~32 tok/s | ~55 tok/s | 5070 fits, 4090 faster |
| Llama 2 13B (Q4_K_M) | ~30 tok/s | ~55 tok/s | 5070 fits tightly |
| CodeLlama 34B (Q4_K_M) | Won’t fit | ~22 tok/s | 4090 only |
| Yi-34B (Q4_K_M) | Won’t fit | ~20 tok/s | 4090 only |
For 7B models the 5070 is usable but not close to the 4090 — roughly a third slower, which is what a 672 GB/s card against a 1,008 GB/s card looks like on bandwidth-bound work. For 13B, the 5070 fits but the 4090 is nearly 2x faster. For 34B, the 5070 does not exist as an option.
The bandwidth story
The 5070’s GDDR7 gives it 672 GB/s versus the 4090’s 1,008 GB/s. That 1.5x gap is the whole story for token generation, because generation re-reads the model out of memory once per token and is bound by bandwidth rather than compute. The 5070’s bandwidth-per-GB-of-capacity is higher (56 versus 42), but that ratio describes how quickly a card can traverse a model that fills it — it does not make a 7B model generate faster on the narrower bus. Where the 5070’s 12GB actually helps is cost, not speed.
For 13B models where both cards fit, the 4090 pulls ahead because its larger VRAM means less memory pressure and its higher total bandwidth wins out.
Which GPU should YOU buy?
Buy the RTX 5070 (~$875) if:
- Your model workload is exclusively 7B and below
- You want modern Blackwell architecture at a reasonable price
- Power efficiency matters (250W vs 450W)
- You run Phi-4 14B as your largest model (fits at Q4 on 12GB)
Buy the RTX 4090 (~$2,200) if:
- You run 13B models regularly — the 4090 is nearly 2x faster and has far more VRAM headroom
- You want to run 34B models at any point
- You value context length — 24GB gives far more KV cache headroom for long prompts
- You want one GPU to handle your full model roster without VRAM constraints through 2027+
The honest advice: If you are choosing between these two for LLM work, the 4090’s $1,050 premium buys you double the VRAM and access to an entirely different tier of models. Unless budget is genuinely constrained, the 4090 is the right answer.
Value framing
| Metric | RTX 5070 | RTX 4090 |
|---|---|---|
| Price | ~$875 | ~$2,200 |
| Max model (single GPU) | 13B Q4 (tight) | 34B Q4 |
| 13B tok/s | ~30 | ~55 |
| 7B tok/s | ~63 | ~95 |
| 34B capable? | No | Yes |
The 5070 gives you about two thirds of the 4090’s 7B speed for 40% of the price. If 7B is your ceiling, that math still works. If you want 13B or beyond, the 5070 is not the right tool.
Common mistakes to avoid
- Assuming newer architecture beats older VRAM capacity for LLM. For image generation and gaming, a newer GPU often beats an older flagship. For LLM inference, VRAM capacity almost always matters more than architecture generation.
- Thinking 12GB is “enough” because your model file is 8GB. The model file size does not equal VRAM consumption. Add KV cache, the inference engine’s overhead, and system memory buffers — the actual VRAM draw for a 13B model at Q4 with 4K context routinely hits 10–11GB. And if you are tempted to push the 5070’s 12GB at 34B, see can the RTX 5070 run 34B? — the short answer is no, but the article shows exactly where it breaks.
- Buying the 5070 planning to “upgrade later.” GPU upgrades are expensive. If you know you will want 34B capability within 2 years, buy the 4090 now instead of paying for a 5070 plus a future upgrade.
- Forgetting the price gap compounds over time. The $1,050 difference between a 5070 and 4090 is real money — but spread over 3–4 years of daily use, the per-day cost difference is small relative to the capability difference.
Final verdict
| Your goal | Best GPU | Price |
|---|---|---|
| 7B only, budget-conscious | RTX 5070 | ~$875 |
| 13B models | RTX 4090 | ~$2,200 |
| 34B models | RTX 4090 | ~$2,200 |
| Max capability | RTX 5090 | ~$4,900 |
VRAM wins for LLM inference. The RTX 4090’s 24GB is not just more headroom — it fundamentally unlocks an entire tier of models that the 5070’s 12GB cannot reach. At ~$875, the 5070 is a solid GPU; for serious LLM work, the 4090 is the correct choice.
Check NVIDIA GeForce RTX 5070 on Amazon→Buy on Shopee SG→For Ollama-specific GPU picks, see our best GPU for Ollama guide. Shopping with a budget cap? Our best GPU for LLM under $1,000 covers strong mid-range options. For the full flagship comparison, see RTX 5090 vs 4090 for LLM. Comparing the 5070 Ti against a used 3090 instead? See RTX 5070 Ti vs 3090 for LLM.