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⇱ RTX 4070 TI SUPER Local LLM Benchmarks, Context Scaling & Supported Models 2026 – Hardware Corner

Tier 1 Enthusiast

RTX 4070 Ti SUPER LLM Performance

Local LLM Performance: 84.3 t/s average on 14B models at 16k context.

Gen (14B 4-bit) 84.3 t/s
PP (14B 4-bit) 3,064 t/s
Max Model 20B
VRAM
16 GB GDDR6X
Bandwidth 672 GB/s
Token Gen (14B @ 4k Ctx)

84.3T/s

Prompt Proc (14B @ 4k Ctx)

3,064T/s

Summary

In our 2026 review, we view the RTX 4070 Ti SUPER as the standout performer of the RTX 40-series mid-range lineup, largely because it finally breaks the 12GB barrier with 16GB of GDDR6X memory. We were impressed by its 672 GB/s bandwidth, which allows it to handle 20B parameter models and long-context workloads far better than its non-Super counterparts. However, at the current price it is hard to justify compared to the RTX 5060 Ti 16GB, which offers better value. We recommend this card primarily if found at a discount or for users specifically building high-bandwidth dual-GPU rigs.

Key Insights

Capable of running gpt-oss 20B (MXFP4) with full 128k context support completely in VRAM.
Features significantly higher memory bandwidth (672 GB/s) compared to other 4070 models.
Scales excellently in dual-GPU setups thanks to the 16GB framebuffer and high bandwidth.
Achieves impressive generation speeds of 57.5 t/s on 20B models even at maximum 128k context.

Current Price in US

$690

Avg. Market Value

Current Pricing

Hardware Specs
VRAM 16GB GDDR6X
Capable of running 20B model
Bandwidth 672 GB/s
Architecture Ada Lovelace
Memory speed 21 Gbps
Memory bus 256 bit
TDP 285 W
Suggested PSU 700 W
Price/GB VRAM $43.13
Price/(t/s) with 14B @ 16k $8.19

Biggest LLMs You Can Run on This GPU

The models below represent the largest language models that fit fully in VRAM on this GPU using 4-bit quantization (GGUF). Benchmarks include token generation and prompt processing speeds measured at their maximum supported context length.

gpt-oss 20B (MXFP4) Max 128k
Token Generation 57.5 t/s @ 128k context
Prompt Processing 993.5 t/s @ 128k context
Qwen3 14B (Q4_K) Max 32k
Token Generation 37.7 t/s @ 32k context
Prompt Processing 1,236.9 t/s @ 32k context
Qwen3 8B (Q4_K) Max 64k
Token Generation 36.6 t/s @ 64k context
Prompt Processing 829.4 t/s @ 64k context

Note: Context values are grouped into standard tiers (4K, 16K, 32K, 64K, 128K). Models may support slightly higher context, but they remain in the lower tier unless they reach the next bracket.

RTX 4070 Ti SUPER local LLM Inference Performance vs Similar GPUs

Compare prompt ingestion and token generation speeds against similar GPUs across widely used local models and extended context lengths up to 256K.

Local LLM Benchmarks

Prompt processing (t/s) and token generation speed (t/s) across different open weight models and context lengths.

Prompt Processing
Model 4k Ctx 16k Ctx 32k Ctx 64k Ctx 128k Ctx 256k Ctx
Qwen3 8B (Q4_K) 5,220.1 3,050.7 1,616.6 829.4 — —
Qwen3 14B (Q4_K) 3,048.0 2,003.4 1,236.9 — — —
gpt-oss 20B (MXFP4) 5,628.1 4,182.7 3,020.0 1,716.2 993.5 —
Token Generation
Model 4k Ctx 16k Ctx 32k Ctx 64k Ctx 128k Ctx 256k Ctx
Qwen3 8B (Q4_K) 96.3 72.2 54.5 36.6 — —
Qwen3 14B (Q4_K) 58.1 47.2 37.7 — — —
gpt-oss 20B (MXFP4) 128.9 113.6 98.7 78.7 57.5 —

Frequently Asked Questions

Common questions about running LLMs on the RTX 4070 Ti SUPER.