01
Qwen3.5 9B Instruct
Qwen / 9B / Q4_K_M / ~7 GB
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: ~47 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.
Best Local AI Models for RTX 4070 (12GB)
The RTX 4070 delivers strong mid-range performance with 12GB GDDR6X memory. At 52 tokens per second for 8B models, it offers excellent speed for 7B-8B parameter models at a reasonable price point.
Quick answer
The best local LLM for the RTX 4070 is Qwen3.5 9B Instruct at ~47 tok/s on its 12GB VRAM. It uses ~7GB of VRAM; the RTX 4070 handles up to 9B parameter models at Q4. A 14B model runs at ~6 tok/s with CPU offload.
$ollama run qwen3.5:9b
TOP PICK
Qwen3.5 9B Instruct
EST. SPEED
~47 tok/s
VRAM NEEDED
~7 GB
Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks.
RTX 4070 Estimated Tokens/sec by Model Size
Q4_K_M · ModelFit estimate| Model Size | Est. Speed | Fit on 12GB |
|---|---|---|
| 7B | ~58 tok/s | Fits in VRAM |
| 14B | ~6 tok/s | CPU offload (slow) |
| 20B MoE (3.6B active) | ~37 tok/s | CPU offload (slow) |
| 32B | ~2 tok/s | CPU offload (slow) |
| 35B MoE (3B active) | ~15 tok/s | CPU offload (slow) |
| 70B | ~1 tok/s | CPU offload (slow) |
| 120B MoE (5.1B active) | ~7 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'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
≈ $579 streetStorage & 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 VRAM for AI: What Actually Fits?
12GB GDDR6X at 504 GB/s makes the RTX 4070 the fastest 12GB card from the previous generation. The higher bandwidth compared to the RTX 3060 (504 vs 360 GB/s) translates directly to faster token generation. You get the same model capacity as the 3060 but 24% more speed. For 7B-9B models at Q4, expect 4-6GB usage with solid headroom for context.
RTX 4070 vs Similar GPUs
| Hardware | Memory | Speed | Bandwidth | Price |
|---|---|---|---|---|
| RTX 3060 | 12 GB | 42 tok/s | 360 GB/s | $250 |
| 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 |
Recommended Models
registry-verified10 models02
Qwen3 8B
Qwen / 8B / Q4_K_M / ~6.5 GB
Best for: Chat, Coding·Pop: 88/100
Perf: ~52 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 4070.
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: ~104 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.
04
Gemma 4 12B
Gemma / 12B / Q4_K_M / ~8 GB
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: ~37 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.
05
Llama 3.1 8B Instruct
Llama / 8B / Q4_K_M / ~6.5 GB
Best for: Chat, Coding·Pop: 78/100
Perf: ~52 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 4070.
06
Qwen2.5 Coder 7B
Qwen / 7B / Q4_K_M / ~5.5 GB
Best for: Coding·Pop: 72/100
Perf: ~58 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for coding on RTX 4070.
07
DeepSeek-R1 Distill Qwen 7B
DeepSeek / 7B / Q4_K_M / ~5.5 GB
Best for: Reasoning, Coding·Pop: 68/100
Perf: ~58 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for reasoning, coding on RTX 4070.
08
Qwen2.5 7B Instruct
Qwen / 7B / Q4_K_M / ~5.5 GB
Best for: Chat, Coding·Pop: 72/100
Perf: ~58 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 4070.
09
Mistral 7B Instruct
Mistral / 7B / Q4_K_M / ~5.5 GB
Best for: Chat, Coding·Pop: 74/100
Perf: ~58 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 4070.
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: ~52 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.
Models Too Big for 12GB? Rent a Cloud GPU
by the hourThe RTX 4070 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 FAQ: Common Questions
How much VRAM does the RTX 4070 have for LLMs?
The RTX 4070 has 12GB GDDR6X VRAM with 504 GB/s bandwidth. About 11.5GB is usable for models. Same capacity as the RTX 3060 but 40% more bandwidth, making it faster for the same models.
What size LLM can I run on an RTX 4070?
Up to 9B parameter models at Q4 quantization, same as other 12GB cards. The advantage is speed: 52 tok/s vs 42 tok/s on the RTX 3060. Popular models include Qwen 2.5 7B, Llama 3.2 8B, and Gemma 2 9B.
Is the RTX 4070 good for local AI?
Yes, it is a strong mid-range choice. The 4070 offers a good balance of speed (52 tok/s) and price. Its main limitation is 12GB VRAM: if you need 14B models, consider the RTX 5070 Ti or 4060 Ti 16GB instead.
RTX 4070 vs RTX 5070 for AI: which should I buy?
The RTX 5070 is 13% faster (59 vs 52 tok/s) at the same $579 MSRP. Both have 12GB VRAM. If buying new, the 5070 is the better pick. If buying used, a discounted 4070 under $400 is excellent value.
How fast is a 27B-class model on the RTX 4070?
The RTX 4070's 12GB of VRAM cannot fit a 32B model comfortably. The largest size class it fits is 7B, at an estimated 58 tok/s.
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