FitMyLLM
▸ SAMSUNG· RDNA2

Samsung Xclipse 920

Running LLMs on the Samsung Xclipse 920 — the long read: which models fit at which quantisation, and the settings worth changing. · Or what a budget buys

VRAM
8 GB
ENTRY-LEVEL
BANDWIDTH
51.2
GB/S
MODELS Q4
201/449
45%
7B Q4 SPEED
~7
SLOW
▸ MODEL COVERAGE @ Q445% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

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

3B
~15
TOK/S
7B
~7
TOK/S
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
8 GB
BANDWIDTH
51.2 GB/s
FP16 COMPUTE
1.5 TFLOPS
TDP
7W
MEMORY
Shared
ARCHITECTURE
RDNA2
46
FAST MODELS · >30 TOK/S
Real-time chat speed
107
USABLE · >10 TOK/S
Comfortable for all tasks
201
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ DON’T WANT TO BUY?

Test Samsung Xclipse 920 (or anything bigger) without committing. Pay by the second, cancel anytime.

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▸ COMPATIBLE MODELS· 201
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
506
TOK/S · 7% VRAM
›
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
337
TOK/S · 7% VRAM
›
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
325
TOK/S · 7% VRAM
›
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
325
TOK/S · 7% VRAM
›
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
178
TOK/S · 8% VRAM
›
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
169
TOK/S · 8% VRAM
›
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
149
TOK/S · 8% VRAM
›
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
136
TOK/S · 9% VRAM
›
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
136
TOK/S · 9% VRAM
›
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
136
TOK/S · 9% VRAM
›
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
130
TOK/S · 9% VRAM
›
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
126
TOK/S · 9% VRAM
›
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
120
TOK/S · 9% VRAM
›
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
91
TOK/S · 10% VRAM
›
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
91
TOK/S · 10% VRAM
›
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
88
TOK/S · 10% VRAM
›
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
80
TOK/S · 10% VRAM
›
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
80
TOK/S · 10% VRAM
›
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
76
TOK/S · 11% VRAM
›
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
76
TOK/S · 11% VRAM
›
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
76
TOK/S · 11% VRAM
›
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
73
TOK/S · 11% VRAM
›
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
62
TOK/S · 12% VRAM
›
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
61
TOK/S · 12% VRAM
›
A
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
56
TOK/S · 12% VRAM
›
A
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
52
TOK/S · 13% VRAM
›
A
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
48
TOK/S · 13% VRAM
›
A
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
42
TOK/S · 14% VRAM
›
A
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
41
TOK/S · 15% VRAM
›
B
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
38
TOK/S · 15% VRAM
›
B
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
38
TOK/S · 15% VRAM
›
B
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
38
TOK/S · 15% VRAM
›
B
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
38
TOK/S · 15% VRAM
›
B
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
35
TOK/S · 16% VRAM
›
B
OPT 1.3B1.3B
OPT·2K CTX· CHAT
35
TOK/S · 16% VRAM
›
B
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
35
TOK/S · 16% VRAM
›
B
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
34
TOK/S · 16% VRAM
›
B
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
32
TOK/S · 17% VRAM
›
B
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
32
TOK/S · 17% VRAM
›
B
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
30
TOK/S · 70% VRAM
›
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your Samsung Xclipse 920.

WHAT THIS CARD IS WORTH

Samsung Xclipse 920 holds 201 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is Falcon3-10B at IQ4_XS.

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
Falcon3-10B10.3B · IQ4_XS7.1 GB7 tok/sEST
Bamba 9B v29.78B · Q4_K_M7.2 GB6 tok/sEST
RecurrentGemma 9B9.63B · Q4_K_M6.8 GB7 tok/sEST
glm-4-9b9.4B · Q4_K_M6.6 GB7 tok/sEST
Yi 1.5 9B9B · Q4_K_M6.5 GB7 tok/sEST
Yi Coder 9B9B · Q4_K_M6.5 GB7 tok/sEST
Ministral 3 8B8.92B · Q4_K_M6.6 GB7 tok/sEST
Ministral 3 8B Reasoning8.92B · Q4_K_M6.6 GB7 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M3 Pro (18GB)12 GB$1,5991.5 tok/s per $100
M1 Pro (16GB)11 GB$9992.9 tok/s per $100
M2 Pro (16GB)11 GB$1,2992.2 tok/s per $100
M4 (16GB)11 GB$4994.0 tok/s per $100
Samsung Xclipse 9208 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

Samsung Xclipse 920 — 8 GB VRAM.

▸ SAMSUNG XCLIPSE 920 SPEC
BRAND
Apple
VRAM
8 GB Shared
BANDWIDTH
51.2 GB/s
FP16 COMPUTE
1.5 TFLOPS
TDP
7 W
ARCHITECTURE
RDNA2
▸ AI CAPABILITY
201/ 449 models @ Q4

With 8 GB VRAM and 51.2 GB/s bandwidth, this GPU handles models up to 9.63B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~6 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR SAMSUNG XCLIPSE 920
201 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
RecurrentGemma 9B9.63B6.4 GB535.0
glm-4-9b9.4B6.2 GB520.5
gemma-2-9b9.2B6.1 GB530.2
Yi 1.5 9B9B6.0 GB530.3
Yi Coder 9B9B6.0 GB535.8
Ministral 3 8B8.92B5.9 GB525.7
Ministral 3 8B Reasoning8.92B5.9 GB5—
NVIDIA-Nemotron-Nano-9B-v28.9B5.9 GB544.2
InternLM3 8B Instruct8.8B5.9 GB538.7
Gemma 1 7B8.54B5.7 GB524.7
CodeGemma 7B8.54B5.7 GB540.2
LFM2 8B A1B8.3B5.6 GB3024.3
Seed-Coder 8B Instruct8.25B5.5 GB634.1
Seed-Coder 8B Reasoning8.25B5.5 GB632.9
DeepSeek R1-0528 Qwen3 8B8.2B5.5 GB636.3
Qwen3-8B8.2B5.5 GB643.3
Granite 3.0 8B8.17B5.5 GB636.4
Granite 3.1 8B8.17B5.5 GB638.6
Command-R7B8.03B5.4 GB635.3
Aya Expanse 8B8B5.4 GB627.8