01
Qwen3.5 9B Instruct (Q8)
Qwen / 9B / Q8_0 / ~10.7 GB
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: ~40 tok/s · first token ~0.4s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 4070 Ti SUPER.
Best Local AI Models for RTX 4070 Ti SUPER (16GB)
The RTX 4070 Ti SUPER packs 16GB GDDR6X and delivers 72 tokens per second for 8B models. A strong performer from the previous generation, offering enough VRAM for 14B models with solid throughput.
Quick answer
The best local LLM for the RTX 4070 Ti SUPER is Qwen3.5 9B Instruct (Q8) at ~40 tok/s on its 16GB VRAM. It uses ~10.7GB of VRAM; the RTX 4070 Ti SUPER handles up to 14b parameter models at Q4. A 14B model runs at ~45 tok/s.
$ollama run qwen3.5:9b-q8_0
TOP PICK
Qwen3.5 9B Instruct (Q8)
EST. SPEED
~40 tok/s
VRAM NEEDED
~10.7 GB
Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks.
RTX 4070 Ti SUPER Estimated Tokens/sec by Model Size
Q4_K_M · ModelFit estimate| Model Size | Est. Speed | Fit on 16GB |
|---|---|---|
| 7B | ~81 tok/s | Fits in VRAM |
| 14B | ~45 tok/s | Fits in VRAM |
| 20B MoE (3.6B active) | ~68 tok/s | Fits in VRAM |
| 32B | ~4 tok/s | CPU offload (slow) |
| 35B MoE (3B active) | ~44 tok/s | CPU offload (slow) |
| 70B | ~1 tok/s | CPU offload (slow) |
| 120B MoE (5.1B active) | ~10 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 Ti SUPER'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 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 4070 Ti SUPER
≈ $1,148 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 Ti SUPER VRAM for AI: What Actually Fits?
16GB GDDR6X at 672 GB/s positions this card between the 5060 Ti and 5070 Ti in bandwidth. It loads 14B Q4 models with room to spare and handles 7B models at 72 tok/s. The main drawback is pricing: at $1,148 MSRP, it costs more than the newer RTX 5070 Ti ($749) which is actually faster. Best bought on the used market where prices have dropped since the RTX 50-series launch.
RTX 4070 Ti SUPER vs Similar GPUs
| Hardware | Memory | Speed | Bandwidth | Price |
|---|---|---|---|---|
| RTX 5070 Ti | 16 GB | 87 tok/s | 896 GB/s | $749 |
| 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 |
| RTX 4080 SUPER | 16 GB | 79 tok/s | 736 GB/s | $1,597 |
Recommended Models
registry-verified10 models02
Gemma 4 12B
Gemma / 12B / Q4_K_M / ~8 GB
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: ~51 tok/s · first token ~0.4s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, coding, multimodal on RTX 4070 Ti SUPER.
03
Qwen3 14B
Qwen / 14B / Q4_K_M / ~11 GB
Best for: Coding, Quality·Pop: 84/100
Perf: ~45 tok/s · first token ~0.4s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for coding, quality on RTX 4070 Ti SUPER.
04
Gemma 3 12B Instruct
Gemma / 12B / Q4_K_M / ~9.5 GB
Best for: Chat, Quality·Pop: 76/100
Perf: ~51 tok/s · first token ~0.4s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, quality on RTX 4070 Ti SUPER.
05
Mistral Nemo 12B
Mistral / 12B / Q4_K_M / ~9.5 GB
Best for: Chat, Translation·Pop: 78/100
Perf: ~51 tok/s · first token ~0.4s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, translation on RTX 4070 Ti SUPER.
06
Llama 3.1 8B Instruct (Q5)
Llama / 8B / Q5_K_M / ~8 GB
Best for: Chat, Coding·Pop: 68/100
Perf: ~62 tok/s · first token ~0.4s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 4070 Ti SUPER.
07
GPT-OSS 20B
GPT-OSS / 21B / MXFP4 / ~13.8 GB
Best for: Chat, Coding, Reasoning·Pop: 85/100
Perf: ~60 tok/s · first token ~0.4s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for chat, coding, reasoning on RTX 4070 Ti SUPER.
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: ~81 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 4070 Ti SUPER.
09
Qwen3.5 9B Instruct
Qwen / 9B / Q4_K_M / ~7 GB
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: ~65 tok/s · first token ~0.4s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 4070 Ti SUPER.
10
Qwen2.5 Coder 14B
Qwen / 14B / Q4_K_M / ~11 GB
Best for: Coding·Pop: 68/100
Perf: ~45 tok/s · first token ~0.4s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for coding on RTX 4070 Ti SUPER.
Models Too Big for 16GB? Rent a Cloud GPU
by the hourThe RTX 4070 Ti SUPER 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 4070 Ti SUPER FAQ: Common Questions
How much VRAM does the RTX 4070 Ti SUPER have for LLMs?
The RTX 4070 Ti SUPER has 16GB GDDR6X VRAM with 672 GB/s bandwidth. About 15.5GB usable for model loading. Fits 14B models at Q4 and all 7B-9B models comfortably.
What size LLM can I run on an RTX 4070 Ti SUPER?
Up to 14B parameter models at Q4 quantization. Same model capacity as other 16GB cards. Speed on 8B models is an estimated 72 tok/s (14B around 45), faster than the 4060 Ti but slower than the newer 5070 Ti.
Is the RTX 4070 Ti SUPER still worth buying for AI in 2026?
At MSRP ($1,148), no. The RTX 5070 Ti costs $749 and is 21% faster. However, used 4070 Ti SUPERs at $600-700 offer good value: you get 16GB VRAM and ~72 tok/s on 8B models at a reasonable price.
RTX 4070 Ti SUPER vs RTX 5070 Ti for local AI?
The RTX 5070 Ti is faster (an estimated 87 vs 72 tok/s on 8B), cheaper ($749 vs $1,148), and uses GDDR7. The 5070 Ti wins on every metric for AI workloads. The only reason to buy the 4070 Ti SUPER is availability or used pricing.
How fast is a 27B-class model on the RTX 4070 Ti SUPER?
The RTX 4070 Ti SUPER'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 68 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.