FitMyLLM
▸ AMD· GCN 2.0

AMD Radeon R9 295X2

Running LLMs on the Radeon R9 295X2 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
4 GB
BUDGET
BANDWIDTH
320
GB/S
MODELS Q4
97/449
22%
7B Q4 SPEED
~41
FAST
▸ MODEL COVERAGE @ Q422% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~95
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
4 GB
BANDWIDTH
320 GB/s
FP16 COMPUTE
5.7 TFLOPS
TDP
500W
MEMORY
GDDR5
ARCHITECTURE
GCN 2.0
STREAM PROCESSORS
2,816
COMPUTE UNITS
44
PCIE
Gen 3 x16
97
FAST MODELS · >30 TOK/S
Real-time chat speed
97
USABLE · >10 TOK/S
Comfortable for all tasks
97
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

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▸ COMPATIBLE MODELS· 97
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
3160
TOK/S · 14% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
2107
TOK/S · 14% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
2032
TOK/S · 14% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
2032
TOK/S · 14% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
1111
TOK/S · 16% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
1053
TOK/S · 16% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
933
TOK/S · 17% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
849
TOK/S · 17% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
849
TOK/S · 17% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
849
TOK/S · 17% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
813
TOK/S · 18% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
790
TOK/S · 18% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
749
TOK/S · 18% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
569
TOK/S · 20% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
569
TOK/S · 20% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
547
TOK/S · 20% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
501
TOK/S · 21% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
501
TOK/S · 21% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
474
TOK/S · 21% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
474
TOK/S · 21% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
474
TOK/S · 21% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
459
TOK/S · 22% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
384
TOK/S · 24% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
379
TOK/S · 24% VRAM
›
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
351
TOK/S · 25% VRAM
›
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
327
TOK/S · 26% VRAM
›
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
303
TOK/S · 27% VRAM
›
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
263
TOK/S · 29% VRAM
›
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
259
TOK/S · 29% VRAM
›
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
237
TOK/S · 31% VRAM
›
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
237
TOK/S · 31% VRAM
›
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
237
TOK/S · 31% VRAM
›
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
237
TOK/S · 31% VRAM
›
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
219
TOK/S · 32% VRAM
›
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
219
TOK/S · 32% VRAM
›
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
219
TOK/S · 32% VRAM
›
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
211
TOK/S · 33% VRAM
›
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
200
TOK/S · 34% VRAM
›
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
200
TOK/S · 34% VRAM
›
S
Qwen2.5-Coder-1.5B1.5B
QWEN·32K CTX· CHAT· TOOL_USE· CODING
190
TOK/S · 35% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Radeon R9 295X2.

WHAT THIS CARD IS WORTH

Radeon R9 295X2 holds 97 of the models in our catalogue and is, in practice, a Q4_K_M card — the largest it takes is InternLM2 5B 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
InternLM2 5B4.5B · Q4_K_M3.6 GB52 tok/sEST
Ministral 3 3B Reasoning4.25B · Q4_K_M3.3 GB54 tok/sEST
Qwen3 4B4B · Q4_K_M3.3 GB57 tok/sEST
Qwen3-4B Instruct 25074B · Q4_K_M3.3 GB57 tok/sEST
Qwen3-Embedding 4B4B · Q4_K_M3.3 GB57 tok/sEST
Nemotron 3 Nano 4B3.97B · Q4_K_M3.4 GB57 tok/sEST
Ministral 3 3B3.85B · Q5_K_M3.5 GB52 tok/sEST
phi-3-mini-4k 3.8B3.8B · Q4_K_M3.4 GB59 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
RTX 3070 Ti8 GB$49912.6 tok/s per $100
RTX 3060 Ti GDDR6X8 GB$39915.8 tok/s per $100
RTX 3070 Ti 8 GB GA1028 GB$59910.5 tok/s per $100
Arc A7508 GB$19910.1 tok/s per $100
Radeon R9 295X24 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

AMD Radeon R9 295X2 — 4 GB VRAM.

▸ RADEON R9 295X2 SPEC
BRAND
AMD
VRAM
4 GB GDDR5
BANDWIDTH
320 GB/s
FP16 COMPUTE
5.7 TFLOPS
FP32 COMPUTE
5.7 TFLOPS
STREAM PROCESSORS
2,816
TDP
500 W
ARCHITECTURE
GCN 2.0
▸ AI CAPABILITY
97/ 449 models @ Q4

With 4 GB VRAM and 320 GB/s bandwidth, this GPU handles models up to 4.25B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~37 tok/s.

§ 01TOP MODELS FOR RADEON R9 295X2
97 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
Ministral 3 3B Reasoning4.25B3.1 GB67—
Qwen 1.5 4B4B2.9 GB7112.6
Qwen3 4B4B2.9 GB7140.7
Qwen3-4B Instruct 25074B2.9 GB7137.2
Qwen3-Embedding 4B4B2.9 GB71—
Nemotron 3 Nano 4B3.97B2.9 GB7232.0
Ministral 3 3B3.85B2.8 GB7421.4
Phi-3.5 Mini 3.8B3.82B2.8 GB7446.6
phi-3-mini-4k 3.8B3.8B2.8 GB7530.5
Phi-4-mini 3.8B3.8B2.8 GB7549.0
Cogito 3B3.61B2.7 GB7922.1
Falcon3-3B3.23B2.5 GB8825.7
granite-4.0-h-micro 3.2B3.2B2.4 GB8918.4
Llama-3.2-3B3.2B2.4 GB8917.9
Falcon-H1 3B3.15B2.4 GB9049.5
Qwen 2.5 3B3.1B2.4 GB9237.2
SmolLM3-3B3.1B2.4 GB9230.5
Ministral 3B3B2.3 GB9529.6
StarCoder2 3B3B2.3 GB959.5
Granite 4.1 3B3B2.3 GB9516.6