01
Qwen3.5 9B Instruct
Qwen / 9B / Q4_K_M / ~7 GB
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: ~51 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 4070 SUPER.
Best Local AI Models for RTX 4070 SUPER (12GB)
The RTX 4070 SUPER improves on the base 4070 with more CUDA cores. At 56 tokens per second, it delivers faster inference while maintaining the 12GB VRAM capacity for efficient 7B-9B models.
Quick answer
The best local LLM for the RTX 4070 SUPER is Qwen3.5 9B Instruct at ~51 tok/s on its 12GB VRAM. It uses ~7GB of VRAM; the RTX 4070 SUPER handles up to 9B parameter models at Q4. A 14B model runs at ~7 tok/s with CPU offload.
$ollama run qwen3.5:9b
TOP PICK
Qwen3.5 9B Instruct
EST. SPEED
~51 tok/s
VRAM NEEDED
~7 GB
Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks.
*Current street price; launch MSRP was $599
RTX 4070 SUPER Estimated Tokens/sec by Model Size
Q4_K_M · ModelFit estimate| Model Size | Est. Speed | Fit on 12GB |
|---|---|---|
| 7B | ~63 tok/s | Fits in VRAM |
| 14B | ~7 tok/s | CPU offload (slow) |
| 20B MoE (3.6B active) | ~40 tok/s | CPU offload (slow) |
| 32B | ~2 tok/s | CPU offload (slow) |
| 35B MoE (3B active) | ~16 tok/s | CPU offload (slow) |
| 70B | ~1 tok/s | CPU offload (slow) |
| 120B MoE (5.1B active) | ~8 tok/s | CPU offload (slow) |
ModelFit estimates, not measured benchmarks: anchored to an 8B-class Q4_K_M model at 16K context on the RTX 4070 SUPER's 504 GB/s bandwidth, then scaled by model size. MoE rows scale by active parameters (decode reads only the active experts), so a 35B MoE runs far faster than a dense 32B. "CPU offload" sizes exceed the 12GB VRAM; dense models slow to a crawl there, MoE models degrade less because hot experts stay GPU-resident.
Context costs VRAM too. Qwen3.5 9B Instruct loads ~7 GB of weights; at 16k context the KV cache adds ~0.5 GB (still fits the ~11 GB usable VRAM), and at 64k it adds ~2.0 GB (still fits).
KV-cache figures assume an fp16 cache, the llama.cpp/Ollama default. Standard GQA models use a size-class estimate (8 KV heads x 128 head dim class); hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next) use the exact per-token cost from their published config, since only their sparse full-attention layers cache KV. A q8_0 KV cache roughly halves either figure. Estimates, not measurements.
Where to Buy the RTX 4070 SUPER
≈ $759 street · Current street price; launch MSRP was $599Storage & accessories for your model library
Internal NVMe SSD · 2TB~$170
A Gen4 M.2 drive keeps your whole GGUF and quant collection on fast local storage, loading models straight off NVMe.
Check price on AmazonUSB4 NVMe Enclosure~$80
40Gbps external storage fast enough to run models from. Pair it with an M.2 drive for a portable model vault.
Check price on AmazonModelFit may earn a commission on purchases through these links, at no extra cost to you. Prices shown are approximate street references.
RTX 4070 SUPER VRAM for AI: What Actually Fits?
Same 12GB GDDR6X and 504 GB/s bandwidth as the base RTX 4070, but extra CUDA cores push compute throughput higher. The result is 56 tok/s vs 52 tok/s, a modest but consistent improvement. VRAM usage is identical to other 12GB cards. Best for users who want peak speed with 7B-9B models and found a good deal on the SUPER variant.
RTX 4070 SUPER vs Similar GPUs
| Hardware | Memory | Speed | Bandwidth | Price |
|---|---|---|---|---|
| RTX 4070 | 12 GB | 52 tok/s | 504 GB/s | $579 |
| RTX 5070 | 12 GB | 59 tok/s | 672 GB/s | $579 |
| RTX 4070 SUPER | 12 GB | 56 tok/s | 504 GB/s | $759 |
| RTX 4070 Ti SUPER | 16 GB | 72 tok/s | 672 GB/s | $1,148 |
Recommended Models
registry-verified10 models02
Qwen3 8B
Qwen / 8B / Q4_K_M / ~6.5 GB
Best for: Chat, Coding·Pop: 88/100
Perf: ~56 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 4070 SUPER.
03
LFM2.5 8B-A1B
LFM2 / 8.3B / Q4_K_M / ~5.5 GB
Best for: On-device agents, tool calling, multilingual chat·Pop: 72/100
Perf: ~112 tok/s · first token ~0.3s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for on-device agents, tool calling, multilingual chat on RTX 4070 SUPER.
04
Gemma 4 12B
Gemma / 12B / Q4_K_M / ~8 GB
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: ~40 tok/s · first token ~0.4s
Local OKOK
Fits in 12 GB VRAM with room to spare. Best for chat, coding, multimodal on RTX 4070 SUPER.
05
Llama 3.1 8B Instruct
Llama / 8B / Q4_K_M / ~6.5 GB
Best for: Chat, Coding·Pop: 78/100
Perf: ~56 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 4070 SUPER.
06
Qwen2.5 Coder 7B
Qwen / 7B / Q4_K_M / ~5.5 GB
Best for: Coding·Pop: 72/100
Perf: ~63 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for coding on RTX 4070 SUPER.
07
DeepSeek-R1 Distill Qwen 7B
DeepSeek / 7B / Q4_K_M / ~5.5 GB
Best for: Reasoning, Coding·Pop: 68/100
Perf: ~63 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for reasoning, coding on RTX 4070 SUPER.
08
Qwen2.5 7B Instruct
Qwen / 7B / Q4_K_M / ~5.5 GB
Best for: Chat, Coding·Pop: 72/100
Perf: ~63 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 4070 SUPER.
09
Mistral 7B Instruct
Mistral / 7B / Q4_K_M / ~5.5 GB
Best for: Chat, Coding·Pop: 74/100
Perf: ~63 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 4070 SUPER.
10
Granite 4.1 8B Instruct
Granite / 8B / Q4_K_M / ~5.5 GB
Best for: Enterprise assistant, tool calling, instruction following·Pop: 62/100
Perf: ~56 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for enterprise assistant, tool calling, instruction following on RTX 4070 SUPER.
Models Too Big for 12GB? Rent a Cloud GPU
by the hourThe RTX 4070 SUPER tops out around up to 9b parameter models. For anything bigger, an hourly rented GPU runs the same open weights with the same Ollama workflow, billed by the hour, no hardware purchase needed.
RunPodHourly GPU pods (RTX 4090 to H100) with one-click Ollama/vLLM templates.Rent
Vast.aiMarketplace of rented GPUs, usually the cheapest per-hour prices.Rent
ModelFit may earn a commission on sign-ups made through these links, at no extra cost to you.
Similar GPUs for Local AI
Compatible Model Families
Qwen
Alibaba Cloud: Widest size range (0.5B to 235B)
LlamaMeta: Most popular open-weight model family
DeepSeekDeepSeek AI: Best-in-class reasoning with R1 models
MistralMistral AI: Excellent performance-per-parameter ratio
GemmaGoogle DeepMind: Excellent quality at small sizes (1B-9B)
PhiMicrosoft: Best quality-per-gigabyte at small sizes
RTX 4070 SUPER FAQ: Common Questions
How much VRAM does the RTX 4070 SUPER have for LLMs?
The RTX 4070 SUPER has 12GB GDDR6X VRAM, identical to the base RTX 4070. Both share 504 GB/s bandwidth. The SUPER variant adds more CUDA cores for faster compute, resulting in 56 tok/s vs 52 tok/s.
What size LLM can I run on an RTX 4070 SUPER?
Up to 9B parameter models at Q4 quantization. Same model capacity as the base 4070 and RTX 3060. The advantage is purely speed: 56 tok/s is 8% faster than the base 4070.
Is the RTX 4070 SUPER worth it over the base 4070 for AI?
At launch, both cards shared the same $599 MSRP. At current street pricing the SUPER runs about $180 higher for an 8% speed boost, a poor trade for AI workloads. Buy it only if the price gap is under $100, or if you also use the GPU for gaming.
RTX 4070 SUPER vs RTX 5070 for local LLMs?
The RTX 5070 is faster (59 vs 56 tok/s) and cheaper ($579 vs $759 at current pricing). Both have 12GB VRAM. The 5070 wins on both price and performance for AI workloads.
How fast is a 27B-class model on the RTX 4070 SUPER?
The RTX 4070 SUPER's 12GB of VRAM cannot fit a 32B model comfortably. The largest size class it fits is 7B, at an estimated 63 tok/s.
The weekly local-AI refresh
New open-weight models, real Apple Silicon benchmarks, and the one model worth running on your Mac this week. Free, one email a week, unsubscribe anytime.
By subscribing you agree to our Privacy Policy and to receive the weekly email. Unsubscribe anytime.
Want Personalized Recommendations?
Use our interactive wizard to compare models across Apple Silicon and NVIDIA GPUs.