Guide · 2026-08-29

Best Local LLM for RTX 4070 / 4070 Super / 4070 Ti (12GB)

Last updated 2026-08-29

All three cards share 12GB of GDDR6X, so the real question isn't which GPU is fastest — it's which model actually fits.

By Mohamed Meguedmi · 8 min read

The RTX 4070 family, spec by spec

The RTX 4070, 4070 Super, and 4070 Ti all sit on the same Ada Lovelace AD104 die, carry 12GB of GDDR6X, and use the same 192-bit memory bus. That last part matters more than it sounds: it means all three cards deliver the exact same 504 GB/s of memory bandwidth, which is the number that actually governs token generation speed once a model is loaded. What changes between the three is CUDA core count and power draw — and the relationship between those two isn't linear in the way you'd hope.

The base RTX 4070 ships with 5,888 CUDA cores at a 200W TDP. Step up to the 4070 Super and you get 7,168 CUDA cores — a 22% increase — for only 20 extra watts (220W total). The 4070 Ti goes further with 7,680 CUDA cores, but it needs 285W to get there, a 30% jump in power draw over the Super for only about 5% more LLM inference performance. For anyone running these cards for local inference rather than gaming, that's a bad trade: more heat, more noise, more strain on a PSU, for a rounding-error speed gain.

Card	CUDA Cores	Memory Bandwidth	TDP	Typical Used Price (2026)
RTX 4070	5,888	504 GB/s	200W	~$320–360
RTX 4070 Super	7,168	504 GB/s	220W	~$380–430
RTX 4070 Ti	7,680	504 GB/s	285W	~$420–470
RTX 4070 Ti Super (16GB)	8,448	672 GB/s	285W	~$500–560

If you want to see how these cards stack up against Ampere, Blackwell, or AMD alternatives on the same VRAM budget, our GPU comparison tool lets you line them up side by side.

What actually fits in 12GB

Twelve gigabytes sounds like a lot until you start loading real models with real context windows. The honest way to think about a 12GB card is in terms of what fits with room to spare, versus what technically loads but leaves you no margin for context, KV cache, or a second process running alongside it.

• Gemma 4 12B, long context: at Q4 quantization with a 32k context window, this lands around 10GB of VRAM — workable on any of these cards, but tight enough that you should enable Q8 KV cache quantization to keep headroom for the OS and display buffers.
• Mistral Small 24B: at Q4 this needs roughly 14GB, which overflows a 12GB card by about 2GB. Once that spillover hits system RAM, throughput collapses to around 15 tok/s — technically it runs, but it's not a model you'd want to use daily on these cards.
• Qwen3.5 27B: at Q4 this needs about 18GB, and even dropping to Q3 quantization still needs roughly 14GB. There's no realistic quant level that gets a 27B-class model onto a 12GB card — it's simply off the table.
• Multi-stage RAG pipelines: running a small generator alongside an embedding model and a reranker simultaneously (for example, an 8B-class model plus an embedder and reranker) adds up to roughly 10GB combined. It's possible on 12GB, but you're running with essentially zero margin — any context growth or a second concurrent request will push you over.

Quantization trade-offs: Q4 vs Q6 vs Q8

On a 12GB card, quantization is the lever you pull to trade a small amount of output quality for VRAM headroom — and it's the difference between a model that fits comfortably and one that overflows. Q4 quantization roughly halves the memory footprint of a model compared to Q8, which is why it's the default choice for anything in the 9B-12B range if you also want a generous context window. Q6 sits in between and is a reasonable compromise when a model is small enough that you have VRAM to spare — you get noticeably better output fidelity than Q4 with only a modest memory cost. Q8 is close to full precision and is worth reserving for smaller models (7B-9B) where you have plenty of headroom left over, since the quality gain over Q6 is smaller than the gain from Q6 over Q4.

KV cache quantization is the other lever worth knowing about. Leaving KV cache at full precision (F16) while running long-context sessions can quietly eat several extra gigabytes as the context fills up. Switching KV cache to Q8 — as recommended for Gemma 4 12B's 32k-context configuration above — recovers meaningful VRAM with a negligible quality hit, and it's often the difference between a model fitting on 12GB or not.

4070 vs Super vs Ti: which one to actually buy

If you're shopping the used market and comparing all three side by side, the 4070 Super is the one to buy. It delivers the best performance-per-dollar of the family, and at a typical used price in the $380–430 range, it's an excellent fit for the 8B-12B model class that actually runs well on 12GB. The base 4070 saves you some money but gives up 22% of the compute for a relatively small discount, and the 4070 Ti asks you to pay more up front and run 30% hotter for a performance gain that's within the margin of a different quant level.

The one scenario where the Ti makes sense is if you find it meaningfully discounted below Super pricing on the used market — otherwise there's no argument for it once you're optimizing for inference rather than gaming benchmarks.

Power supply and case considerations

None of these three cards are particularly demanding on a PSU — even the 4070 Ti's 285W TDP is manageable on a quality 650W unit with headroom for a modern CPU. Where it matters more is sustained load: local inference sessions tend to run a GPU at or near full power draw for minutes at a time (unlike bursty gaming loads), so airflow and case ventilation matter more for consistent throughput than they do for gaming rigs built around the same card.

Getting a model running: the fast path

The quickest way to get any of these models running locally is through Ollama, which handles quantization, GPU offload, and context configuration automatically.

# Windows
winget install Ollama.Ollama
ollama run qwen3.5:9b
ollama run gemma4:12b       # test the 12B multimodal variant

Start with the 9B-12B class models listed above before experimenting with anything larger — if a model refuses to load or falls back to CPU offload, that's usually your first sign it's too big for a 12GB card at the quant level you picked. If you want to model expected VRAM usage and cost-per-token before buying hardware, our cost calculator and full hardware configurator can help you plan a build around a specific model target rather than guessing.

The upgrade path: new RTX 5070 or a used Ti Super

If you're buying new rather than used, the RTX 5070 is worth serious consideration. At its $549 MSRP, it keeps the same 12GB VRAM ceiling as the 4070 family but adds roughly 35% faster LLM inference along with FP4 support, which matters increasingly as more quantized model formats target it directly. It's the better new-purchase option over any of the 4070 variants at this point in their lifecycle.

If you're shopping used and specifically want to run 24B-class models like Mistral Small without the VRAM-overflow penalty, skip the 12GB cards entirely and look for a used RTX 4070 Ti Super with 16GB instead. That extra 4GB is exactly what's needed to fit Mistral Small 24B at Q4 without spilling into system RAM, and used pricing typically lands in the $500–560 range — a meaningful step up from a 4070 Super, but the only option in this GPU generation that meaningfully changes what model classes are accessible. For readers who'd rather skip the 24B compromise altogether and go straight to a rig built for larger models, our dual RTX 3090 budget build guide covers a 48GB-VRAM alternative at a comparable used-market cost.

Verdict

Across the RTX 4070 family, the Super is the clear pick: best performance-per-dollar, modest 220W power draw, and a used price that makes it an easy recommendation for anyone running 8B-12B models. The base 4070 is a reasonable budget fallback if the Super isn't discounted enough to justify the gap, and the 4070 Ti isn't worth chasing unless it's priced at or below Super levels — the extra CUDA cores just don't translate into a proportional inference gain. If you're buying new, put the RTX 5070's $549 MSRP and FP4 support ahead of any used 4070 variant. If your goal is specifically to run 24B-class models like Mistral Small, the 16GB 4070 Ti Super is the one card in this lineup that actually changes the equation. For the full underlying spec data referenced in this guide, including bandwidth and TDP figures across GPU generations, see our GPU comparison hub — the same dataset is also available programmatically through the BestLLMfor public API (CC BY 4.0) and our open-source MCP server for anyone building tooling on top of it.