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Computed

What LLMs can the GeForce RTX 4070 Ti SUPER run?

43 of 62 rostered models have a usable on-device fit. The table keeps all 62 visible, including 1 below-context fits and 18 that exceed this device, so “not listed” never masquerades as an answer.

Published memory

16 GB

GDDR6X

Assumed usable

14.7 GiB

92% of capacity

Peak bandwidth

672 GB/s

theoretical device spec

Usable fits

43 / 62

largest: 30.5B at Q3_K_M

Every model, solved on this accelerator

Usable rows fit weights plus at least 4k tokens, capped at the model’s own trained window. Tight rows hold weights but fall below that floor. The speed column is a memory-bandwidth ceiling, not a benchmark.

43 usable · 1 tight · 18 no fit

ModelParametersOn-device resultSelected weightsMax contextDecode ceiling · read per token
Qwen3 30B-A3BQwen30.5BQuantized fitQ3_K_M · 13.7 GiBpublished file11k278 tok/sreads 2.3 GiB/token
Gemma 3 27B InstructGemma27.4BQuantized fitQ3_K_M · 12.5 GiBarchitecture calculation23k46 tok/sreads 13.5 GiB/token
Gemma 2 27B InstructGemma27.2BQuantized fitQ3_K_M · 12.5 GiBpublished file6k43 tok/sreads 14.7 GiB/token
Dolphin Mistral 24B Venice EditionMistral24BQuantized fitQ4_K_M · 13.5 GiBarchitecture calculation8k43 tok/sreads 14.7 GiB/token
Mistral Small 24B InstructMistral23.6BQuantized fitQ4_K_M · 13.3 GiBpublished file9k43 tok/sreads 14.6 GiB/token
gpt-oss 20Bgpt-oss21.5BFull precision · comfortableAs released · 12.8 GiBpublished file81k227 tok/sreads 2.8 GiB/token
DeepSeek-R1-Distill-Qwen 14BDeepSeek14.8BQuantized fitQ6_K · 11.3 GiBpublished file18k49 tok/sreads 12.8 GiB/token
Qwen2.5 14B InstructQwen14.8BQuantized fitQ6_K · 11.3 GiBpublished file18k49 tok/sreads 12.8 GiB/token
Qwen3 14BQwen14.8BQuantized fitQ6_K · 11.3 GiBpublished file22k50 tok/sreads 12.5 GiB/token
Phi-4 14BPhi14.7BQuantized fitQ6_K · 11.2 GiBpublished file16k49 tok/sreads 12.8 GiB/token
OLMo 2 13B InstructOLMo13.7BQuantized fitQ6_K · 10.5 GiBpublished file4k46 tok/sreads 13.6 GiB/token
Mistral Nemo 12B InstructMistral12.2BQuantized fitQ8_0 · 12.1 GiBpublished file17k47 tok/sreads 13.4 GiB/token
Gemma 3 12B InstructGemma12.2BQuantized fitQ8_0 · 12.1 GiBarchitecture calculation38k49 tok/sreads 12.9 GiB/token
Qwythos 9B Claude Mythos 5 1MQwen3 5 Text9.4BQuantized fitQ8_0 · 9.3 GiBarchitecture calculation43k61 tok/sreads 10.3 GiB/token
Gemma 2 9B InstructGemma9.2BQuantized fitQ8_0 · 9.2 GiBpublished file8k53 tok/sreads 11.8 GiB/token
Qwen3 8BQwen8.2BQuantized fitQ8_0 · 8.1 GiBpublished file40k68 tok/sreads 9.2 GiB/token
Granite 3.3 8B InstructGranite8.2BQuantized fitQ8_0 · 8.1 GiBarchitecture calculation42k67 tok/sreads 9.3 GiB/token
DeepSeek-R1-Distill-Llama 8BDeepSeek8BQuantized fitQ8_0 · 8.0 GiBpublished file54k70 tok/sreads 9.0 GiB/token
Llama 3.1 8B InstructLlama8BQuantized fitQ8_0 · 8.0 GiBpublished file54k70 tok/sreads 9.0 GiB/token
DeepSeek-R1-Distill-Qwen 7BDeepSeek7.6BFull precision · limited contextFP16 / BF16 · 14.2 GiBpublished file10k43 tok/sreads 14.6 GiB/token
Qwen2.5 7B InstructQwen7.6BFull precision · limited contextFP16 / BF16 · 14.2 GiBpublished file10k43 tok/sreads 14.6 GiB/token
Qwen2.5-Coder 7B InstructQwen7.6BFull precision · limited contextFP16 / BF16 · 14.2 GiBpublished file10k43 tok/sreads 14.6 GiB/token
Falcon3 7B InstructFalcon7.5BFull precision · limited contextFP16 / BF16 · 13.9 GiBpublished file8k43 tok/sreads 14.7 GiB/token
Mistral 7B Instruct v0.3Mistral7.2BFull precision · limited contextFP16 / BF16 · 13.5 GiBpublished file10k43 tok/sreads 14.5 GiB/token
Gemma 3 4B InstructGemma4.3BFull precision · comfortableFP16 / BF16 · 8.0 GiBpublished file128k76 tok/sreads 8.3 GiB/token
Nanbeige4.2 3BNanbeige4.2BFull precision · comfortableFP16 / BF16 · 7.8 GiBpublished file81k74 tok/sreads 8.5 GiB/token
Qwen3 4BQwen4BFull precision · comfortableFP16 / BF16 · 7.5 GiBpublished file40k73 tok/sreads 8.6 GiB/token
Phi-4-mini 3.8B InstructPhi3.8BFull precision · comfortableFP16 / BF16 · 7.1 GiBpublished file61k77 tok/sreads 8.1 GiB/token
Phi-3.5-mini 3.8B InstructPhi3.8BFull precision · limited contextFP16 / BF16 · 7.1 GiBpublished file20k62 tok/sreads 10.1 GiB/token
Llama 3.2 3B InstructLlama3.2BFull precision · comfortableFP16 / BF16 · 6.0 GiBpublished file80k91 tok/sreads 6.9 GiB/token
Qwen2.5 3B InstructQwen3.1BFull precision · comfortableFP16 / BF16 · 5.7 GiBpublished file32k104 tok/sreads 6.0 GiB/token
Qwen3 1.7BQwen2BFull precision · comfortableFP16 / BF16 · 3.8 GiBpublished file40k134 tok/sreads 4.7 GiB/token
DeepSeek-R1-Distill-Qwen 1.5BDeepSeek1.8BFull precision · comfortableFP16 / BF16 · 3.3 GiBpublished file128k177 tok/sreads 3.5 GiB/token
SmolLM2 1.7B InstructSmolLM1.7BFull precision · comfortableFP16 / BF16 · 3.2 GiBpublished file8k134 tok/sreads 4.7 GiB/token
Qwen2.5 1.5B InstructQwen1.5BFull precision · comfortableFP16 / BF16 · 2.9 GiBpublished file32k202 tok/sreads 3.1 GiB/token
Llama 3.2 1B InstructLlama1.2BFull precision · comfortableFP16 / BF16 · 2.3 GiBpublished file128k245 tok/sreads 2.6 GiB/token
TinyLlama 1.1B ChatTinyLlama1.1BFull precision · comfortableFP16 / BF16 · 2.0 GiBpublished file2k299 tok/sreads 2.1 GiB/token
MiniCPM5 1BLlama1.1BFull precision · comfortableFP16 / BF16 · 2.0 GiBpublished file128k284 tok/sreads 2.2 GiB/token
Gemma 3 1B InstructGemma1000MFull precision · comfortableFP16 / BF16 · 1.9 GiBpublished file32k329 tok/sreads 1.9 GiB/token
Qwen3 0.6BQwen752MFull precision · comfortableFP16 / BF16 · 1.4 GiBpublished file40k275 tok/sreads 2.3 GiB/token
Qwen2.5 0.5B InstructQwen494MFull precision · comfortableFP16 / BF16 · 942 MiBpublished file32k617 tok/sreads 1.0 GiB/token
SmolLM2 360M InstructSmolLM362MFull precision · comfortableFP16 / BF16 · 690 MiBpublished file8k634 tok/sreads 1010 MiB/token
SmolLM2 135M InstructSmolLM135MFull precision · comfortableFP16 / BF16 · 257 MiBpublished file8k1,468 tok/sreads 437 MiB/token
Laguna XS 2.1Laguna33.4BBelow usable contextQ3_K_M · 14.5 GiBpublished file4k469 tok/sreads 1.3 GiB/token
Hy3HY V3298.8BNo on-device fitQ3_K_M · 128.1 GiBpublished fileNot resident
Laguna S 2.1Laguna117.6BNo on-device fitQ3_K_M · 50.3 GiBpublished fileNot resident
gpt-oss 120Bgpt-oss116.8BNo on-device fitQ3_K_M · 58.3 GiBpublished fileNot resident
Qwen2.5 72B InstructQwen72.7BNo on-device fitQ3_K_M · 35.1 GiBpublished fileNot resident
DeepSeek-R1-Distill-Llama 70BDeepSeek70.6BNo on-device fitQ3_K_M · 31.9 GiBpublished fileNot resident
Llama 3.1 70B InstructLlama70.6BNo on-device fitQ3_K_M · 31.9 GiBpublished fileNot resident
Llama 3.1 Nemotron 70B InstructNemotron70.6BNo on-device fitQ3_K_M · 31.9 GiBpublished fileNot resident
Llama 3.3 70B InstructLlama70.6BNo on-device fitQ3_K_M · 31.9 GiBpublished fileNot resident
Mixtral 8x7B InstructMistral46.7BNo on-device fitQ3_K_M · 21.3 GiBarchitecture calculationNot resident
Hermes 4.3 36BSeed OSS36.2BNo on-device fitQ3_K_M · 16.5 GiBarchitecture calculationNot resident
KAT Coder V2.5 DevQwen3 5 MOE Text34.7BNo on-device fitQ3_K_M · 15.8 GiBarchitecture calculationNot resident
Qwen AgentWorld 35B A3BQwen3 5 MOE Text34.7BNo on-device fitQ3_K_M · 15.5 GiBpublished fileNot resident
Yi 1.5 34B ChatYi34.4BNo on-device fitQ3_K_M · 15.5 GiBpublished fileNot resident
DeepSeek-R1-Distill-Qwen 32BDeepSeek32.8BNo on-device fitQ3_K_M · 14.8 GiBpublished fileNot resident
Qwen2.5 32B InstructQwen32.8BNo on-device fitQ3_K_M · 14.8 GiBpublished fileNot resident
Qwen2.5-Coder 32B InstructQwen32.8BNo on-device fitQ3_K_M · 14.8 GiBpublished fileNot resident
QwQ 32BQwen32.8BNo on-device fitQ3_K_M · 14.9 GiBarchitecture calculationNot resident
Qwen3 32BQwen32.8BNo on-device fitQ3_K_M · 14.9 GiBarchitecture calculationNot resident

Device specification and assumption

Memory
16 GB GDDR6X
Bandwidth
672 GB/s
Bus
256-bit × 21 Gbps
Usable budget
92% → 14.7 GiB

Capacity and bandwidth come from the vendor specification. The usable fraction is an explicit planning assumption, not a device specification.

What the table does — and does not — claim

Weight sizes use published checkpoint or GGUF files where the roster has one, otherwise the documented bits-per-weight calculation. Context comes from each model’s layer and KV-head geometry. The decode figure is the card’s peak bandwidth divided by the bytes one token reads — the weights it routes through plus one pass over the KV cache, printed under each ceiling — so it is a roofline bound, not measured application throughput. Multi-GPU splitting and host-memory offload are outside this on-device table.

Questions about this card

What LLMs can the GeForce RTX 4070 Ti SUPER run?

43 of the 62 open models in this roster have an on-device configuration with usable context. The largest by exact parameter count is Qwen3 30B-A3B at Q3_K_M, with room for 11k tokens. “Largest” describes parameter count, not model quality or task performance.

How much memory is usable on the GeForce RTX 4070 Ti SUPER?

The device publishes 16 GB of GDDR6X. This calculator budgets 92%, or 14.7 GiB, for model weights and KV cache; the remainder is an explicit allowance for the runtime, driver or operating system, workspace, and display. It is an assumption rather than a vendor specification.

Are the speed figures benchmarks for the GeForce RTX 4070 Ti SUPER?

No. They are bandwidth-bound roofline ceilings: 672 GB/s divided by the bytes one decoded token actually reads, which the table prints beside every ceiling. That is not the size of the file on disk — a token reads the weights it routes through, all of them for a dense model but only the selected experts for a mixture-of-experts, plus one pass over the KV cache. Laguna XS 2.1 holds 14.5 GiB resident at Q3_K_M yet reads 1.3 GiB/token, which is what produces its 469 tok/s. Real throughput is lower because kernels, cache traffic, prompt processing, scheduling, and runtime overhead also consume time.