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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 3070 8GB
NVIDIA

NVIDIA

RTX 3070 8GB

RTX 30ConsumerAmperePCIe 4CUDA
8GB
VRAM
448GB/s
Bandwidth
40TFLOPS
FP16 Compute
320TOPS
INT8 Inference
$499 MSRP
VRAM8 GBBandwidth448 GB/sCompute40 TFInference320 TOPSValue8.02 TF/$k
RTX 3070 8GBCategory AvgIntel Arc B570 10GB

Specifications

Compute
FP1640 TFLOPS
INT8320 TOPS
ArchitectureAmpere
Memory
VRAM8 GB
Bandwidth448 GB/s
General
FamilyRTX 30
SegmentConsumer
InterconnectPCIe 4
Compute PlatformCUDA
MSRP$499

Architecture

Ampere

Ampere is NVIDIA's second-generation RTX architecture, built on Samsung's 8nm process. It introduced 3rd-generation Tensor Cores with support for sparsity-accelerated INT8 operations and improved FP16 throughput over Turing.

AI Relevance

Sparsity-aware Tensor Cores can effectively double throughput for structured sparse workloads. However, the lack of FP8 support means quantized inference is less efficient than Ada Lovelace or Blackwell.

Process: Samsung 8nmPlatform: CUDATensor Cores: Gen 3Precisions: FP32, FP16, BF16, INT8, INT4

Recommendations by Workload

Agentic Coding

C

StarCoder2 3B

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.

Decode 171.4 tok/s · 57K ctx · llama.cpp
4.5 GB / 8.0 GB VRAM

Chat

C

Qwen 3 4B

This model is a direct match for chat. It sits in the middle of the current model mix. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 128.5 tok/s · 13K ctx · llama.cpp
4.9 GB / 8.0 GB VRAM

Coding

C

Codestral Mamba 7B

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 should run, but memory headroom will be limited. Known channels: huggingface, ollama.

Decode 73.4 tok/s · 18K ctx · llama.cpp
7.1 GB / 8.0 GB VRAM

RAG

B

Phi 4 Mini 4B

This model is still usable for rag, but it is not the most specialized pick. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 128.5 tok/s · 47K ctx · llama.cpp
5.4 GB / 8.0 GB VRAM

Reasoning

C

Phi 4 Mini 4B

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 128.5 tok/s · 26K ctx · llama.cpp
4.9 GB / 8.0 GB VRAM

Full Model Compatibility

UnslothQwen3.5 4B
B55
4B4.9 GB129 tok/s26K ctx
dense
Lmstudio-communityLgemma 3 4b it
B55
4B4.9 GB129 tok/s26K ctx
dense
BartowskiBLlama 3.2 3B Instruct
C55
3B4.7 GB148 tok/s27K ctx
dense
QwenQwen2.5 3B Instruct
C54
3B4.3 GB171 tok/s30K ctx
dense
TheBlokeTLlama 2 7B Chat
C53
7B7.1 GB73 tok/s18K ctx
dense
TheBlokeTMistral 7B Instruct v0.2
C53
7B7.1 GB73 tok/s18K ctx
dense
MaziyarPanahiMMistral 7B Instruct v0.3
C53
7B7.1 GB73 tok/s18K ctx
dense
BartowskiBgemma 2 2b it
C53
2B4.1 GB201 tok/s31K ctx
dense
BartowskiBMeta Llama 3.1 8B Instruct
C53
8B7.8 GB64 tok/s16K ctx
dense
XtunerXllava llama 3 8b v1 1
C53
8B7.8 GB64 tok/s16K ctx
dense
UnslothDeepSeek R1 0528 Qwen3 8B
C53
8B7.8 GB64 tok/s16K ctx
dense
MaziyarPanahiMMeta Llama 3 8B Instruct
C53
8B7.8 GB64 tok/s16K ctx
dense
Googlegemma 2b
C52
2B3.7 GB257 tok/s34K ctx
dense
TheDrummerTGemmasutra Mini 2B v1
C52
2B3.7 GB257 tok/s34K ctx
dense
QwenQwen2.5 1.5B Instruct
C51
1.5B3.4 GB314 tok/s37K ctx
dense
Hugging-quantsHLlama 3.2 1B Instruct Q8 0
C51
1B3.3 GB329 tok/s39K ctx
dense
TheBlokeTTinyLlama 1.1B Chat v1.0
C51
1.1B3.2 GB314 tok/s40K ctx
dense
Ggml-orgGSmolVLM 500M Instruct
C50
0.5B2.9 GB329 tok/s44K ctx
dense
Ggml-orgGembeddinggemma 300M
C49
0.3B2.7 GB329 tok/s47K ctx
dense
UnslothQwen3.5 9B
C43
9B8.6 GB54 tok/s15K ctx
dense
HauhauCSHQwen3.5 9B Uncensored HauhauCS Aggressive
C43
9B8.6 GB54 tok/s15K ctx
dense
Lmstudio-communityLQwen3.5 9B
C42
9B8.6 GB54 tok/s15K ctx
dense
DeepSeekDeepSeek R1 671B
F0
671B416.8 GB2 tok/s4K ctx
moe
MistralDevstral 2 123B Instruct
F0
123B95.9 GB4 tok/s4K ctx
dense
Z.aiGLM-5
F0
744B461.8 GB2 tok/s4K ctx
moe
UnslothQwen3.5 27B
F0
27B22.4 GB19 tok/s6K ctx
dense
UnslothQwen3.5 35B A3B
F0
35B28.5 GB15 tok/s4K ctx
dense
Moonshot AIKimi K2.5
F0
1000B616.7 GB2 tok/s4K ctx
moe
MistralMistral Large 3
F0
675B419.9 GB2 tok/s4K ctx
+1moe
MistralMistral Small 4 119B
F0
119B75.3 GB13 tok/s4K ctx
moe
AlibabaQwen3-Coder 30B A3B Instruct
F0
30.5B21.1 GB44 tok/s6K ctx
moe
AlibabaQwen3-Coder 480B A35B Instruct
F0
480B300.0 GB3 tok/s4K ctx
moe
AlibabaQwen3-Coder-Next
F0
80B51.3 GB20 tok/s4K ctx
moe
UnslothQwen3.5 122B A10B
F0
122B80.5 GB5 tok/s4K ctx
dense
DeepSeekDeepSeek V3 671B
F0
671B416.8 GB2 tok/s4K ctx
moe
MistralMixtral 8x22B
F0
141B93.8 GB7 tok/s4K ctx
moe
AlibabaQwen 2.5 72B
F0
72B56.9 GB7 tok/s4K ctx
dense
AlibabaQwen 3 235B A22B
F0
235B148.5 GB6 tok/s4K ctx
moe
AlibabaQwen3-VL 30B A3B Instruct
F0
30B20.8 GB45 tok/s6K ctx
moe
UnslothQwen3.5 397B A17B
F0
397B305.9 GB2 tok/s4K ctx
dense
MistralDevstral Small 2 24B Instruct
F0
24B20.1 GB21 tok/s6K ctx
dense
MetaLlama 3.3 70B
F0
70B55.3 GB7 tok/s4K ctx
dense
MetaLlama 4 Maverick 17B 128E
F0
400B248.4 GB4 tok/s4K ctx
moe
CohereCommand A 111B
F0
111B86.8 GB5 tok/s4K ctx
dense
AlibabaQwen 2.5 Coder 32B
F0
32B26.2 GB16 tok/s5K ctx
dense
AlibabaQwen 2.5 VL 72B
F0
72B56.9 GB7 tok/s4K ctx
dense
Unslothgemma 3 27b it
F0
27B22.4 GB19 tok/s6K ctx
dense
Lmstudio-communityLQwen3.5 35B A3B
F0
35B28.5 GB15 tok/s4K ctx
dense
MistralCodestral 2 25.08
F0
22B18.6 GB23 tok/s7K ctx
dense
MistralDevstral Small 1.1
F0
24B20.1 GB21 tok/s6K 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 ~422.6 GB
MistralDevstral 2 123B Instruct
123BTier 5Needs ~115.2 GB
Runs on Mac Studio M3 Ultra 256GB
Z.aiGLM-5
744BTier 5Needs ~468.0 GB
UnslothQwen3.5 27B
27BTier 5Needs ~26.6 GB
Runs on RTX 5090 32GB (~$1,999)
UnslothQwen3.5 35B A3B
35BTier 5Needs ~34.0 GB
Runs on Mac mini M4 64GB (~$1,099)

Upgrade paths

Upgrade from RTX 3070 8GB

See what you unlock with more powerful hardware

Upgrade options

Upgrade options

IntelIntel Arc B570 10GBNext step up
10 GB VRAM (+2)
A
Unlocks Qwen3.5 9B, Qwen3.5 9B Uncensored HauhauCS Aggressive, Qwen3.5 9B+20 more

 

NVIDIAGTX 1080 Ti 11GBNVIDIA upgrade
11 GB VRAM (+3)484 GB/s (+36)
A
Unlocks Qwen3.5 9B, Qwen3.5 9B Uncensored HauhauCS Aggressive, Qwen3.5 9B+26 more

 

AMDRX 7600 XT 16GBBest value
16 GB VRAM (+8)
A
Unlocks Qwen3.5 9B, Qwen3.5 9B Uncensored HauhauCS Aggressive, Qwen3.5 9B+57 more

~$329 MSRP

AMDAMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+280)8000 GB/s (+7552)
A
Unlocks Devstral 2 123B Instruct, Qwen3.5 27B, Qwen3.5 35B A3B+141 more · +1116% faster avg

~$8,000 MSRP

Compare this GPU