01
Qwen3.5 9B Instruct
Qwen / 9B / Q4_K_M / ~7 GB
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: ~53 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 5070.
Best Local AI Models for RTX 5070 (12GB)
The RTX 5070 brings Blackwell architecture to the mid-range. At 59 tokens per second for 8B models, it edges out the 4070 SUPER while keeping 12GB VRAM. Best for users prioritizing speed with 7B-9B models.
Quick answer
The best local LLM for the RTX 5070 is Qwen3.5 9B Instruct at ~53 tok/s on its 12GB VRAM. It uses ~7GB of VRAM; the RTX 5070 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
~53 tok/s
VRAM NEEDED
~7 GB
Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks.
RTX 5070 Estimated Tokens/sec by Model Size
Q4_K_M · ModelFit estimate| Model Size | Est. Speed | Fit on 12GB |
|---|---|---|
| 7B | ~66 tok/s | Fits in VRAM |
| 14B | ~7 tok/s | CPU offload (slow) |
| 20B MoE (3.6B active) | ~42 tok/s | CPU offload (slow) |
| 32B | ~2 tok/s | CPU offload (slow) |
| 35B MoE (3B active) | ~17 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 5070's 672 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 5070
≈ $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 5070 VRAM for AI: What Actually Fits?
12GB GDDR7 at 672 GB/s makes the RTX 5070 the fastest 12GB card for AI inference. Compared to the RTX 4070 SUPER (504 GB/s), bandwidth jumps 33%. This directly translates to faster token generation: 59 tok/s vs 56 tok/s. The 12GB limit means 14B models still require quantization tricks. For 7B-9B workloads, this is the sweet spot if you do not need the extra VRAM.
RTX 5070 vs Similar GPUs
| Hardware | Memory | Speed | Bandwidth | Price |
|---|---|---|---|---|
| RTX 5070 | 12 GB | 59 tok/s | 672 GB/s | $579 |
| RTX 4070 | 12 GB | 52 tok/s | 504 GB/s | $579 |
| RTX 5070 Ti | 16 GB | 87 tok/s | 896 GB/s | $749 |
| 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: ~59 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 5070.
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: ~118 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 5070.
04
Gemma 4 12B
Gemma / 12B / Q4_K_M / ~8 GB
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: ~42 tok/s · first token ~0.4s
Local OKOK
Fits in 12 GB VRAM with room to spare. Best for chat, coding, multimodal on RTX 5070.
05
Llama 3.1 8B Instruct
Llama / 8B / Q4_K_M / ~6.5 GB
Best for: Chat, Coding·Pop: 78/100
Perf: ~59 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 5070.
06
Qwen2.5 Coder 7B
Qwen / 7B / Q4_K_M / ~5.5 GB
Best for: Coding·Pop: 72/100
Perf: ~66 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for coding on RTX 5070.
07
DeepSeek-R1 Distill Qwen 7B
DeepSeek / 7B / Q4_K_M / ~5.5 GB
Best for: Reasoning, Coding·Pop: 68/100
Perf: ~66 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for reasoning, coding on RTX 5070.
08
Qwen2.5 7B Instruct
Qwen / 7B / Q4_K_M / ~5.5 GB
Best for: Chat, Coding·Pop: 72/100
Perf: ~66 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 5070.
09
Mistral 7B Instruct
Mistral / 7B / Q4_K_M / ~5.5 GB
Best for: Chat, Coding·Pop: 74/100
Perf: ~66 tok/s · first token ~0.4s
Local OKExcellent
Fits in 12 GB VRAM with room to spare. Best for chat, coding on RTX 5070.
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: ~59 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 5070.
Models Too Big for 12GB? Rent a Cloud GPU
by the hourThe RTX 5070 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 5070 FAQ: Common Questions
How much VRAM does the RTX 5070 have for LLMs?
The RTX 5070 has 12GB GDDR7 VRAM with 672 GB/s bandwidth, 33% faster than the RTX 4070 SUPER. About 11.5GB is usable for model loading. Best suited for 7B-9B models at Q4 quantization.
What size LLM can I run on an RTX 5070?
Up to 9B parameter models at Q4 quantization. This includes Qwen 2.5 7B, Llama 3.2 8B, Mistral 7B, and Gemma 2 9B. The RTX 5070 runs them all at 59 tok/s, the fastest 12GB card available.
Is the RTX 5070 good for local AI in 2026?
The RTX 5070 is the best 12GB card for local AI in 2026. Blackwell architecture and GDDR7 deliver 59 tok/s, 40% faster than the RTX 3060. At $579, it offers strong value if 7B-9B models meet your needs.
Should I get RTX 5070 (12GB) or RTX 5070 Ti (16GB)?
If you want to run 14B models, get the 5070 Ti (16GB). If 7B-9B models are enough, the RTX 5070 saves $170 and is only 32% slower than the Ti. The Ti also has 33% more bandwidth (896 vs 672 GB/s).
RTX 5070 vs RTX 3060 for running AI models?
The RTX 5070 is 40% faster (59 vs 42 tok/s) with the same 12GB VRAM. However, the 3060 costs less than half the price. For budget builds, the 3060 remains excellent. For best speed at 12GB, the 5070 wins.
How fast is a 27B-class model on the RTX 5070?
The RTX 5070's 12GB of VRAM cannot fit a 32B model comfortably. The largest size class it fits is 7B, at an estimated 66 tok/s.
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