FitMyLLM
▸ NVIDIA· KEPLER

NVIDIA GeForce GTX 780

Running LLMs on the GeForce GTX 780 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
3 GB
BUDGET
BANDWIDTH
288
GB/S
MODELS Q4
81/449
18%
7B Q4 SPEED
~33
FAST
▸ MODEL COVERAGE @ Q418% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

Average speeds at Q4 quantization. Actual performance varies by model architecture and context length.

3B
~77
TOK/S
7B
—
3.9GB NEEDED
14B
—
7.9GB NEEDED
32B
—
18.0GB NEEDED
70B
—
39.4GB NEEDED
▸ MEASURED RIG REPORTS

We rent the machine and time every model on it: decode, VRAM peak, concurrency, watts and cost per million tokens. Yours may already be one of them — and two are free to read in full.

SEE THE REPORTS →
▸ SPECIFICATIONS
VRAM
3 GB
BANDWIDTH
288 GB/s
FP16 COMPUTE
4.2 TFLOPS
TDP
250W
MEMORY
GDDR5
ARCHITECTURE
Kepler
CUDA CORES
2,304
PCIE
Gen 3 x16
81
FAST MODELS · >30 TOK/S
Real-time chat speed
81
USABLE · >10 TOK/S
Comfortable for all tasks
81
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test GeForce GTX 780 (or anything bigger) without committing. Pay by the second, cancel anytime.

Spin up in ~60s. Pay by the second. Cancel anytime.

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▸ COMPATIBLE MODELS· 81
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
2560
TOK/S · 18% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
1707
TOK/S · 19% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
1646
TOK/S · 19% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
1646
TOK/S · 19% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
900
TOK/S · 21% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
853
TOK/S · 22% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
755
TOK/S · 22% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
688
TOK/S · 23% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
688
TOK/S · 23% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
688
TOK/S · 23% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
658
TOK/S · 23% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
640
TOK/S · 24% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
606
TOK/S · 24% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
461
TOK/S · 26% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
461
TOK/S · 26% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
443
TOK/S · 27% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
406
TOK/S · 28% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
406
TOK/S · 28% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
384
TOK/S · 29% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
384
TOK/S · 29% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
384
TOK/S · 29% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
372
TOK/S · 29% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
311
TOK/S · 31% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
307
TOK/S · 32% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
284
TOK/S · 33% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
265
TOK/S · 34% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
245
TOK/S · 35% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
213
TOK/S · 38% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
209
TOK/S · 39% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
192
TOK/S · 41% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
192
TOK/S · 41% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
192
TOK/S · 41% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
192
TOK/S · 41% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
177
TOK/S · 43% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
177
TOK/S · 43% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
177
TOK/S · 43% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
171
TOK/S · 44% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
162
TOK/S · 45% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
162
TOK/S · 45% VRAM
›
S
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
154
TOK/S · 47% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your GeForce GTX 780.

WHAT THIS CARD IS WORTH

GTX 780 holds 81 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is Falcon3-3B at Q4_K_M.

TOKENS/SEC PER $100
—
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
—
what memory costs on this card
THE BIGGEST IT TAKES
Falcon3-3B3.23B · Q4_K_M2.6 GB80 tok/sEST
granite-4.0-h-micro 3.2B3.2B · Q4_K_M2.6 GB81 tok/sEST
Llama-3.2-3B3.2B · Q4_K_M2.7 GB81 tok/sEST
Falcon-H1 3B3.15B · Q5_K_M2.7 GB71 tok/sEST
Qwen 2.5 3B3.1B · Q5_K_M2.6 GB72 tok/sEST
SmolLM3-3B3.1B · Q4_K_M2.5 GB84 tok/sEST
Ministral 3B3B · Q4_K_M2.6 GB86 tok/sEST
StarCoder2 3B3B · Q5_K_M2.5 GB74 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M2 (8GB)5 GB$5992.8 tok/s per $100
M3 (8GB)5 GB$5992.8 tok/s per $100
M1 (8GB)5 GB$4992.2 tok/s per $100
RTX 2060 6GB6 GB$15026.7 tok/s per $100
GTX 7803 GB——

Neighbours in memory rather than in price: memory decides whether a card can do the job at all, so two cards of the same size at different prices is the comparison you are making. Ordered by memory, then bandwidth — not by the value column, which is worked out from bandwidth and price alone and therefore rewards a cheap card whatever its software stack does to that bandwidth in practice. Read it as one input, not as a ranking.

▸ DEVICE UNDER TEST

NVIDIA GeForce GTX 780 — 3 GB VRAM.

▸ GEFORCE GTX 780 SPEC
BRAND
NVIDIA
VRAM
3 GB GDDR5
BANDWIDTH
288 GB/s
FP16 COMPUTE
4.2 TFLOPS
FP32 COMPUTE
4.2 TFLOPS
CUDA CORES
2,304
TDP
250 W
ARCHITECTURE
Kepler
▸ AI CAPABILITY
81/ 449 models @ Q4

With 3 GB VRAM and 288 GB/s bandwidth, this GPU handles models up to 3.1B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~33 tok/s.

§ 01TOP MODELS FOR GEFORCE GTX 780
81 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Qwen 2.5 3B3.1B2.4 GB7437.2
SmolLM3-3B3.1B2.4 GB7430.5
Ministral 3B3B2.3 GB7729.6
StarCoder2 3B3B2.3 GB779.5
Granite 4.1 3B3B2.3 GB7716.6
xLAM-2 3B Function-Calling3B2.3 GB77—
Jamba 2 3B3B2.3 GB77—
Dolly v2 3B2.8B2.2 GB825.6
StableLM Zephyr 3B2.79B2.2 GB8314.9
Zephyr 3B2.79B2.2 GB8314.4
OPT 2.7B2.7B2.1 GB8528.0
Phi-2 2.7B2.7B2.1 GB8524.1
Zamba2 2.7B2.7B2.1 GB8548.0
Granite 3.0 2B2.63B2.1 GB8835.8
gemma-2-2b2.6B2.1 GB8922.9
LFM2 2.6B2.6B2.1 GB8916.3
Granite 3.1 2B2.53B2.0 GB9137.8
Granite 3.3 2B2.53B2.0 GB9120.5
Gemma 1 2B2.51B2.0 GB9220.2
CodeGemma 2B2.51B2.0 GB9222.9