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All estimates are approximations based on mathematical models and public specifications. Actual performance may vary. Do not make purchasing decisions based solely on these estimates.

Data sourced from Hugging Face, Ollama, and official model documentation. Model names and logos are trademarks of their respective owners.

© 2026 Will It Run AI — Fase Consulting Ibiza, S.L. (NIF: B57969656)

Home/Hardware/GPUs/RTX 5070 Ti 16GB
NVIDIA

NVIDIA

RTX 5070 Ti 16GB

RTX 50ConsumerBlackwellPCIe 5CUDA
16GB
VRAM
896GB/s
Bandwidth
44TFLOPS
FP16 Compute
704TOPS
INT8 Inference
$749 MSRP
VRAM16 GBBandwidth896 GB/sCompute44 TFInference704 TOPSValue5.87 TF/$k
RTX 5070 Ti 16GBCategory AvgRTX 4000 Ada 20GB

Specifications

Compute
FP1644 TFLOPS
INT8704 TOPS
ArchitectureBlackwell
Memory
VRAM16 GB
Bandwidth896 GB/s
General
FamilyRTX 50
SegmentConsumer
InterconnectPCIe 5
Compute PlatformCUDA
MSRP$749

Architecture

Blackwell

Blackwell is NVIDIA's fifth-generation RTX architecture, built on TSMC's 4NP process. It introduces 5th-generation Tensor Cores with native FP4 precision support, enabling double the inference throughput per watt compared to Ada Lovelace's FP8 operations. Key innovations include the Neural Rendering Pipeline for AI-driven shading and the debut of GDDR7 memory in consumer GPUs.

AI Relevance

FP4 Tensor Cores deliver the highest tokens-per-watt efficiency in any consumer architecture. Native FP4 quantization means models can run at lower precision with minimal quality loss, effectively doubling the effective VRAM for model weights.

Process: TSMC 4NPPlatform: CUDATensor Cores: Gen 5Precisions: FP32, FP16, BF16, FP8, FP4, INT8, INT4

Recommendations by Workload

Agentic Coding

B

Yi Coder 9B

This model is still usable for agentic-coding, but it is not the most specialized pick. It sits in the middle of the current model mix. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 104.5 tok/s · 47K ctx · llama.cpp
10.8 GB / 16.0 GB VRAM

Chat

C

Qwen 3 8B

This model is a direct match for chat. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 117.5 tok/s · 16K ctx · llama.cpp
8.2 GB / 16.0 GB VRAM

Coding

C

Yi Coder 9B

This model is still usable for coding, but it is not the most specialized pick. It sits in the middle of the current model mix. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 104.5 tok/s · 27K ctx · llama.cpp
9.4 GB / 16.0 GB VRAM

RAG

B

granite 8b code instruct 4k

This model is a direct match for rag. It sits in the middle of the current model mix. It fits natively with comfortable headroom.

Decode 117.5 tok/s · 52K ctx · llama.cpp
9.9 GB / 16.0 GB VRAM

Reasoning

C

Qwen 3 8B

This model is a direct match for reasoning. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 117.5 tok/s · 30K ctx · llama.cpp
8.6 GB / 16.0 GB VRAM

Full Model Compatibility

UnslothQwen3.5 9B
C55
9B9.4 GB105 tok/s27K ctx
dense
HauhauCSHQwen3.5 9B Uncensored HauhauCS Aggressive
C55
9B9.4 GB105 tok/s27K ctx
dense
Lmstudio-communityLQwen3.5 9B
C55
9B9.4 GB105 tok/s27K ctx
dense
BartowskiBMeta Llama 3.1 8B Instruct
C54
8B8.6 GB118 tok/s30K ctx
dense
XtunerXllava llama 3 8b v1 1
C54
8B8.6 GB118 tok/s30K ctx
dense
UnslothDeepSeek R1 0528 Qwen3 8B
C54
8B8.6 GB118 tok/s30K ctx
dense
MaziyarPanahiMMeta Llama 3 8B Instruct
C54
8B8.6 GB118 tok/s30K ctx
dense
TheBlokeTLlama 2 7B Chat
C53
7B7.9 GB134 tok/s33K ctx
dense
TheBlokeTMistral 7B Instruct v0.2
C53
7B7.9 GB134 tok/s33K ctx
dense
MaziyarPanahiMMistral 7B Instruct v0.3
C53
7B7.9 GB134 tok/s33K ctx
dense
UnslothQwen3.5 4B
C50
4B5.7 GB235 tok/s45K ctx
dense
Lmstudio-communityLgemma 3 4b it
C50
4B5.7 GB235 tok/s45K ctx
dense
BartowskiBLlama 3.2 3B Instruct
C50
3B5.5 GB271 tok/s47K ctx
dense
QwenQwen2.5 3B Instruct
C49
3B5.1 GB313 tok/s50K ctx
dense
BartowskiBgemma 2 2b it
C49
2B4.9 GB367 tok/s52K ctx
dense
Googlegemma 2b
C48
2B4.5 GB470 tok/s57K ctx
dense
TheDrummerTGemmasutra Mini 2B v1
C48
2B4.5 GB470 tok/s57K ctx
dense
QwenQwen2.5 1.5B Instruct
C48
1.5B4.2 GB573 tok/s61K ctx
dense
Hugging-quantsHLlama 3.2 1B Instruct Q8 0
C48
1B4.1 GB602 tok/s62K ctx
dense
TheBlokeTTinyLlama 1.1B Chat v1.0
C47
1.1B4.0 GB573 tok/s64K ctx
dense
Ggml-orgGSmolVLM 500M Instruct
C47
0.5B3.7 GB602 tok/s69K ctx
dense
Ggml-orgGembeddinggemma 300M
C47
0.3B3.5 GB602 tok/s72K ctx
dense
DeepSeekDeepSeek R1 671B
F0
671B417.6 GB4 tok/s4K ctx
moe
MistralDevstral 2 123B Instruct
F0
123B96.7 GB8 tok/s4K ctx
dense
Z.aiGLM-5
F0
744B462.6 GB4 tok/s4K ctx
moe
UnslothQwen3.5 27B
F0
27B23.2 GB35 tok/s11K ctx
dense
UnslothQwen3.5 35B A3B
F0
35B29.3 GB27 tok/s9K ctx
dense
Moonshot AIKimi K2.5
F0
1000B617.5 GB3 tok/s4K ctx
moe
MistralMistral Large 3
F0
675B420.7 GB4 tok/s4K ctx
+1moe
MistralMistral Small 4 119B
F0
119B76.1 GB23 tok/s4K ctx
moe
AlibabaQwen3-Coder 30B A3B Instruct
F0
30.5B21.9 GB80 tok/s12K ctx
moe
AlibabaQwen3-Coder 480B A35B Instruct
F0
480B300.8 GB6 tok/s4K ctx
moe
AlibabaQwen3-Coder-Next
F0
80B52.1 GB36 tok/s5K ctx
moe
UnslothQwen3.5 122B A10B
F0
122B81.3 GB9 tok/s4K ctx
dense
DeepSeekDeepSeek V3 671B
F0
671B417.6 GB4 tok/s4K ctx
moe
MistralMixtral 8x22B
F0
141B94.6 GB13 tok/s4K ctx
moe
AlibabaQwen 2.5 72B
F0
72B57.7 GB13 tok/s4K ctx
dense
AlibabaQwen 3 235B A22B
F0
235B149.3 GB11 tok/s4K ctx
moe
AlibabaQwen3-VL 30B A3B Instruct
F0
30B21.6 GB83 tok/s12K ctx
moe
UnslothQwen3.5 397B A17B
F0
397B306.7 GB2 tok/s4K ctx
dense
MistralDevstral Small 2 24B Instruct
F0
24B20.9 GB39 tok/s12K ctx
dense
MetaLlama 3.3 70B
F0
70B56.1 GB13 tok/s5K ctx
dense
MetaLlama 4 Maverick 17B 128E
F0
400B249.2 GB7 tok/s4K ctx
moe
CohereCommand A 111B
F0
111B87.6 GB9 tok/s4K ctx
dense
AlibabaQwen 2.5 Coder 32B
F0
32B27.0 GB29 tok/s9K ctx
dense
AlibabaQwen 2.5 VL 72B
F0
72B57.7 GB13 tok/s4K ctx
dense
Unslothgemma 3 27b it
F0
27B23.2 GB35 tok/s11K ctx
dense
Lmstudio-communityLQwen3.5 35B A3B
F0
35B29.3 GB27 tok/s9K ctx
dense
MistralCodestral 2 25.08
F0
22B19.4 GB43 tok/s13K ctx
dense
MistralDevstral Small 1.1
F0
24B20.9 GB39 tok/s12K ctx
dense

Just out of reach

Models you could run with an upgrade

High-quality models that need a bit more memory

DeepSeekDeepSeek R1 671B
671BTier 5Needs ~423.4 GB
MistralDevstral 2 123B Instruct
123BTier 5Needs ~116.0 GB
Runs on Mac Studio M3 Ultra 256GB
Z.aiGLM-5
744BTier 5Needs ~468.8 GB
UnslothQwen3.5 27B
27BTier 5Needs ~27.4 GB
Runs on RTX 5090 32GB (~$1,999)
UnslothQwen3.5 35B A3B
35BTier 5Needs ~34.8 GB
Runs on Mac mini M4 64GB (~$1,099)

Upgrade paths

Upgrade from RTX 5070 Ti 16GB

See what you unlock with more powerful hardware

Upgrade options

Upgrade options

NVIDIARTX 4000 Ada 20GBNext step up
20 GB VRAM (+4)
A
Unlocks Qwen3-Coder 30B A3B Instruct, Qwen3-VL 30B A3B Instruct, Codestral 2 25.08+15 more

 

NVIDIANVIDIA A10 24GBNVIDIA upgrade
24 GB VRAM (+8)
B
Unlocks Qwen3.5 27B, Qwen3-Coder 30B A3B Instruct, Qwen3-VL 30B A3B Instruct+32 more

 

AppleMac mini M4 64GBBest value
64 GB Unified (+48)
B
Unlocks Qwen3.5 27B, Qwen3.5 35B A3B, Qwen3-Coder 30B A3B Instruct+51 more

~$1,099 MSRP

AMDAMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+272)8000 GB/s (+7104)
A
Unlocks Devstral 2 123B Instruct, Qwen3.5 27B, Qwen3.5 35B A3B+81 more · +690% faster avg

~$8,000 MSRP

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