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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/NVIDIA A16 64GB
NVIDIA

NVIDIA

NVIDIA A16 64GB

Ampere DatacenterDatacenterAmperePCIe 4CUDA
64GB
VRAM
600GB/s
Bandwidth
78TFLOPS
FP16 Compute
624TOPS
INT8 Inference
VRAM64 GBBandwidth600 GB/sCompute78 TFInference624 TOPS
NVIDIA A16 64GBCategory AvgNVIDIA A100 80GB

Specifications

Compute
FP1678 TFLOPS
INT8624 TOPS
ArchitectureAmpere
Memory
VRAM64 GB
Bandwidth600 GB/s
General
FamilyAmpere Datacenter
SegmentDatacenter
InterconnectPCIe 4
Compute PlatformCUDA

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

Qwen3-Coder-Next

This model is still usable for agentic-coding, but it is not the most specialized pick. It belongs to a current frontier family for local AI. It should run, but memory headroom will be limited. Known channels: huggingface, ollama, lm-studio.

Decode 29.1 tok/s · 36K ctx · llama.cpp
57.0 GB / 64.0 GB VRAM

Chat

C

Qwen 3 32B

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 24.0 tok/s · 17K ctx · llama.cpp
29.3 GB / 64.0 GB VRAM

Coding

C

Qwen3-Coder-Next

This model is a direct match for coding. It belongs to a current frontier family for local AI. It should run, but memory headroom will be limited. Known channels: huggingface, ollama, lm-studio.

Decode 29.1 tok/s · 18K ctx · llama.cpp
56.9 GB / 64.0 GB VRAM

RAG

C

Command R 35B

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

Decode 21.9 tok/s · 52K ctx · llama.cpp
39.6 GB / 64.0 GB VRAM

Reasoning

C

Qwen 3 32B

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 24.0 tok/s · 32K ctx · llama.cpp
31.8 GB / 64.0 GB VRAM

Full Model Compatibility

AlibabaQwen3-Coder-Next
C51
80B56.9 GB29 tok/s18K ctx
moe
AlibabaQwen3-Coder 30B A3B Instruct
C50
30.5B26.7 GB65 tok/s38K ctx
moe
AlibabaQwen3-VL 30B A3B Instruct
C50
30B26.4 GB67 tok/s39K ctx
moe
UnslothQwen3.5 35B A3B
C49
35B34.1 GB22 tok/s30K ctx
dense
Lmstudio-communityLQwen3.5 35B A3B
C49
35B34.1 GB22 tok/s30K ctx
dense
AlibabaQwen 2.5 Coder 32B
C48
32B31.8 GB24 tok/s32K ctx
dense
UnslothQwen3.5 27B
C48
27B28.0 GB28 tok/s37K ctx
dense
Unslothgemma 3 27b it
C48
27B28.0 GB28 tok/s37K ctx
dense
MetaLlama 3.3 70B
C47
70B60.9 GB11 tok/s17K ctx
dense
MistralDevstral Small 2 24B Instruct
C47
24B25.7 GB32 tok/s40K ctx
dense
AlibabaQwen 2.5 72B
C47
72B62.5 GB11 tok/s16K ctx
dense
MistralDevstral Small 1.1
C47
24B25.7 GB32 tok/s40K ctx
dense
AlibabaQwen 2.5 VL 72B
C47
72B62.5 GB11 tok/s16K ctx
dense
MistralCodestral 2 25.08
C47
22B24.2 GB35 tok/s42K ctx
dense
UnslothQwen3.5 9B
C47
9B14.2 GB85 tok/s72K ctx
dense
HauhauCSHQwen3.5 9B Uncensored HauhauCS Aggressive
C47
9B14.2 GB85 tok/s72K ctx
dense
BartowskiBMeta Llama 3.1 8B Instruct
C47
8B13.4 GB96 tok/s76K ctx
dense
XtunerXllava llama 3 8b v1 1
C47
8B13.4 GB96 tok/s76K ctx
dense
Lmstudio-communityLQwen3.5 9B
C47
9B14.2 GB85 tok/s72K ctx
dense
UnslothDeepSeek R1 0528 Qwen3 8B
C47
8B13.4 GB96 tok/s76K ctx
dense
MaziyarPanahiMMeta Llama 3 8B Instruct
C47
8B13.4 GB96 tok/s76K ctx
dense
TheBlokeTLlama 2 7B Chat
C46
7B12.7 GB110 tok/s81K ctx
dense
TheBlokeTMistral 7B Instruct v0.2
C46
7B12.7 GB110 tok/s81K ctx
dense
MaziyarPanahiMMistral 7B Instruct v0.3
C46
7B12.7 GB110 tok/s81K ctx
dense
UnslothQwen3.5 4B
C46
4B10.5 GB192 tok/s97K ctx
dense
BartowskiBLlama 3.2 3B Instruct
C46
3B10.3 GB221 tok/s100K ctx
dense
Lmstudio-communityLgemma 3 4b it
C46
4B10.5 GB192 tok/s97K ctx
dense
BartowskiBgemma 2 2b it
C45
2B9.7 GB300 tok/s105K ctx
dense
QwenQwen2.5 3B Instruct
C45
3B9.9 GB256 tok/s103K ctx
dense
Googlegemma 2b
C45
2B9.3 GB384 tok/s110K ctx
dense
TheDrummerTGemmasutra Mini 2B v1
C45
2B9.3 GB384 tok/s110K ctx
dense
QwenQwen2.5 1.5B Instruct
C45
1.5B9.0 GB468 tok/s114K ctx
dense
Hugging-quantsHLlama 3.2 1B Instruct Q8 0
C45
1B8.9 GB491 tok/s115K ctx
dense
TheBlokeTTinyLlama 1.1B Chat v1.0
C45
1.1B8.8 GB468 tok/s117K ctx
dense
Ggml-orgGSmolVLM 500M Instruct
C45
0.5B8.5 GB491 tok/s120K ctx
dense
Ggml-orgGembeddinggemma 300M
C45
0.3B8.3 GB491 tok/s123K ctx
dense
DeepSeekDeepSeek R1 671B
F0
671B422.4 GB3 tok/s4K ctx
moe
MistralDevstral 2 123B Instruct
F0
123B101.5 GB6 tok/s10K ctx
dense
Z.aiGLM-5
F0
744B467.4 GB3 tok/s4K ctx
moe
Moonshot AIKimi K2.5
F0
1000B622.3 GB2 tok/s4K ctx
moe
MistralMistral Large 3
F0
675B425.5 GB3 tok/s4K ctx
+1moe
MistralMistral Small 4 119B
F0
119B80.9 GB19 tok/s13K ctx
moe
AlibabaQwen3-Coder 480B A35B Instruct
F0
480B305.6 GB5 tok/s4K ctx
moe
UnslothQwen3.5 122B A10B
F0
122B86.1 GB7 tok/s12K ctx
dense
DeepSeekDeepSeek V3 671B
F0
671B422.4 GB3 tok/s4K ctx
moe
MistralMixtral 8x22B
F0
141B99.4 GB10 tok/s10K ctx
moe
AlibabaQwen 3 235B A22B
F0
235B154.1 GB9 tok/s7K ctx
moe
UnslothQwen3.5 397B A17B
F0
397B311.5 GB2 tok/s4K ctx
dense
MetaLlama 4 Maverick 17B 128E
F0
400B254.0 GB6 tok/s4K ctx
moe
CohereCommand A 111B
F0
111B92.4 GB7 tok/s11K 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 ~428.2 GB
MistralDevstral 2 123B Instruct
123BTier 5Needs ~120.8 GB
Runs on Mac Studio M3 Ultra 256GB
Z.aiGLM-5
744BTier 5Needs ~473.6 GB
Moonshot AIKimi K2.5
1000BTier 5Needs ~627.3 GB
MistralMistral Large 3
675BTier 5Needs ~431.9 GB

Upgrade paths

Upgrade from NVIDIA A16 64GB

See what you unlock with more powerful hardware

Upgrade options

Upgrade options

NVIDIANVIDIA A100 80GBNext step up
80 GB VRAM (+16)2039 GB/s (+1439)
A
Unlocks Mistral Small 4 119B, Llama 4 Scout 17B 16E+264% faster avg

~$15,000 MSRP

NVIDIANVIDIA H100 80GBNVIDIA upgrade
80 GB VRAM (+16)3350 GB/s (+2750)
A
Unlocks Mistral Small 4 119B, Llama 4 Scout 17B 16E+498% faster avg

~$40,000 MSRP

AMDAMD Instinct MI325X 256GBBiggest leap
256 GB VRAM (+192)6000 GB/s (+5400)
A
Unlocks Devstral 2 123B Instruct, Mistral Small 4 119B, Qwen3.5 122B A10B+14 more · +789% faster avg

 

AMDAMD Instinct MI350X 288GBBest value
288 GB VRAM (+224)8000 GB/s (+7400)
A
Unlocks Devstral 2 123B Instruct, Mistral Small 4 119B, Qwen3-Coder 480B A35B Instruct+15 more · +1082% faster avg

~$8,000 MSRP

Compare this GPU