01
Qwen3.5 9B Instruct (Q8)
Qwen / 9B / Q8_0 / ~10.7 GB
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: ~49 tok/s · first token ~0.4s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 5070 Ti.
Best Local AI Models for RTX 5070 Ti (16GB)
The RTX 5070 Ti is the new-generation sweet spot for local AI. With 16GB GDDR7 VRAM it matches the RTX 3090 in speed (~87 tokens per second on 8B models) and offers enough memory for 14B parameter models at an estimated 54 tok/s. Excellent value at $749.
Quick answer
The best local LLM for the RTX 5070 Ti is Qwen3.5 9B Instruct (Q8) at ~49 tok/s on its 16GB VRAM. It uses ~10.7GB of VRAM; the RTX 5070 Ti handles up to 14B parameter models at Q4. A 14B model runs at ~54 tok/s.
$ollama run qwen3.5:9b-q8_0
TOP PICK
Qwen3.5 9B Instruct (Q8)
EST. SPEED
~49 tok/s
VRAM NEEDED
~10.7 GB
Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks.
RTX 5070 Ti Estimated Tokens/sec by Model Size
Q4_K_M · ModelFit estimate| Model Size | Est. Speed | Fit on 16GB |
|---|---|---|
| 7B | ~97 tok/s | Fits in VRAM |
| 14B | ~54 tok/s | Fits in VRAM |
| 20B MoE (3.6B active) | ~83 tok/s | Fits in VRAM |
| 32B | ~5 tok/s | CPU offload (slow) |
| 35B MoE (3B active) | ~53 tok/s | CPU offload (slow) |
| 70B | ~1 tok/s | CPU offload (slow) |
| 120B MoE (5.1B active) | ~12 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 Ti's 896 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 16GB 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 (Q8) loads ~10.7 GB of weights; at 16k context the KV cache adds ~0.5 GB (still fits the ~14 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 Ti
≈ $749 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 Ti VRAM for AI: What Actually Fits?
16GB GDDR7 at 896 GB/s delivers remarkable throughput. The RTX 5070 Ti loads 14B models at Q4 with 5-6GB to spare and runs them at an estimated 54 tok/s. On 8B models its bandwidth pushes tokens out at ~87 tok/s, matching the legendary RTX 3090 (24GB). The 896 GB/s bandwidth is 33% higher than the RTX 5080 on a per-GB basis, making this card incredibly efficient for models that fit its 16GB capacity.
RTX 5070 Ti vs Similar GPUs
| Hardware | Memory | Speed | Bandwidth | Price |
|---|---|---|---|---|
| RTX 5070 | 12 GB | 59 tok/s | 672 GB/s | $579 |
| RTX 5070 Ti | 16 GB | 87 tok/s | 896 GB/s | $749 |
| RTX 5080 | 16 GB | 94 tok/s | 960 GB/s | $999 |
| RTX 4070 Ti SUPER | 16 GB | 72 tok/s | 672 GB/s | $1,148 |
Recommended Models
registry-verified10 models02
Gemma 4 12B
Gemma / 12B / Q4_K_M / ~8 GB
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: ~62 tok/s · first token ~0.4s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, coding, multimodal on RTX 5070 Ti.
03
Qwen3 14B
Qwen / 14B / Q4_K_M / ~11 GB
Best for: Coding, Quality·Pop: 84/100
Perf: ~54 tok/s · first token ~0.4s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for coding, quality on RTX 5070 Ti.
04
Gemma 3 12B Instruct
Gemma / 12B / Q4_K_M / ~9.5 GB
Best for: Chat, Quality·Pop: 76/100
Perf: ~62 tok/s · first token ~0.4s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, quality on RTX 5070 Ti.
05
Mistral Nemo 12B
Mistral / 12B / Q4_K_M / ~9.5 GB
Best for: Chat, Translation·Pop: 78/100
Perf: ~62 tok/s · first token ~0.4s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, translation on RTX 5070 Ti.
06
Llama 3.1 8B Instruct (Q5)
Llama / 8B / Q5_K_M / ~8 GB
Best for: Chat, Coding·Pop: 68/100
Perf: ~75 tok/s · first token ~0.4s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 5070 Ti.
07
GPT-OSS 20B
GPT-OSS / 21B / MXFP4 / ~13.8 GB
Best for: Chat, Coding, Reasoning·Pop: 85/100
Perf: ~73 tok/s · first token ~0.4s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for chat, coding, reasoning on RTX 5070 Ti.
08
LFM2 24B-A2B Instruct
LFM2 / 24B / Q4_K_M / ~14 GB
Best for: Local AI agents, privacy-first tool calling, MCP workflows·Pop: 80/100
Perf: ~98 tok/s · first token ~0.4s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for local ai agents, privacy-first tool calling, mcp workflows on RTX 5070 Ti.
09
Qwen3.5 9B Instruct
Qwen / 9B / Q4_K_M / ~7 GB
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: ~79 tok/s · first token ~0.4s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 5070 Ti.
10
Qwen2.5 Coder 14B
Qwen / 14B / Q4_K_M / ~11 GB
Best for: Coding·Pop: 68/100
Perf: ~54 tok/s · first token ~0.4s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for coding on RTX 5070 Ti.
Models Too Big for 16GB? Rent a Cloud GPU
by the hourThe RTX 5070 Ti tops out around up to 14b 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 Ti FAQ: Common Questions
How much VRAM does the RTX 5070 Ti have for LLMs?
The RTX 5070 Ti has 16GB GDDR7 VRAM with 896 GB/s bandwidth. About 15.5GB is usable for models. This comfortably fits all 14B models at Q4 and some 27B models at lower quantization.
What size LLM can I run on an RTX 5070 Ti?
Up to 14B parameter models at Q4 quantization fit perfectly. Popular picks: Qwen 2.5 14B, DeepSeek-R1 14B, Phi-3 14B. You can also squeeze in 27B models at Q3, though with reduced quality.
Is the RTX 5070 Ti good for local AI?
The RTX 5070 Ti is arguably the best value GPU for local AI in 2026. At $749, it matches the RTX 3090 in 8B speed (~87 tok/s est.), runs 14B models at an estimated 54 tok/s, and costs significantly less than the RTX 5080 ($999).
RTX 5070 Ti vs RTX 3090 for AI: which is better?
The RTX 3090 has 24GB VRAM vs 16GB, allowing 32B models. But the 5070 Ti matches it in 8B speed (~87 tok/s est.) and costs less new. Choose the 3090 (used, ~$900) for 32B models, or the 5070 Ti for 14B models with modern efficiency.
RTX 5070 Ti vs RTX 5080 for running LLMs?
Both have 16GB VRAM. The 5080 is 8% faster (an estimated 94 vs 87 tok/s on 8B) but costs $250 more ($999 vs $749). For most users, the 5070 Ti delivers 93% of the speed at 75% of the price, a clear value winner.
How fast is a 27B-class model on the RTX 5070 Ti?
The RTX 5070 Ti's 16GB of VRAM cannot fit a 32B model comfortably. The largest size class it fits is 20B MoE (3.6B active), at an estimated 83 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.