Speed in AI coding is easy to oversimplify. Teams often talk about a model or backend as if it is simply fast or slow, but real coding workflows split speed into at least two different questions: how quickly the first useful token arrives, and how much work the system can sustain once generation is underway.<\/p>\n\n\n\n
A recent Cline benchmark made that split very visible. In a short elimination-style task, a cloud-backed setup won because it started fastest. In a longer raw inference test, a local DGX Spark setup delivered far stronger sustained throughput than a consumer GPU running the same model with heavy memory offloading. For teams choosing where to run coding agents, that distinction matters a lot.<\/p>\n\n\n\n
The hardware details help explain the gap. NVIDIA’s DGX Spark system overview<\/a> highlights its 128 GB unified memory design, while the test’s 4090 machine had 24 GB of VRAM and had to offload much of a 120B model into system RAM. That changes the whole shape of the workload.<\/p>\n\n\n\n In a tiny sequential task, time-to-first-token decides the winner. The first system to understand the prompt, generate a valid command, and execute it gets a head start that the others may never recover from. That is exactly what happened in the short Cline test.<\/p>\n\n\n\n Cloud infrastructure can shine here because the backend is already optimized for fast response paths. If your workload is mostly quick classifications, short prompts, or tiny agent loops where the first answer matters more than the long run, low TTFT can beat a stronger local machine.<\/p>\n\n\n\n Most coding sessions are not one-second knife fights. They are long, messy loops with file edits, tool calls, retries, test runs, and hundreds or thousands of generated tokens. That is where sustained throughput starts to matter more than the opening burst.<\/p>\n\n\n\n At 42.9 tokens per second, the DGX Spark result shows what happens when a large model can stay in fast memory. By contrast, the 4090 result shows how expensive offloading becomes when the model is too large for local VRAM. The same model family can feel radically different depending on memory layout, not just raw GPU brand or price.<\/p>\n\n\n\n If you work with local stacks, the Ollama documentation<\/a> is a good reference for how teams expose local and cloud-backed model endpoints in a compatible way. The important lesson is not which tool you pick. It is that model size, memory fit, and network topology change the user experience much more than a single benchmark headline suggests.<\/p>\n\n\n\n The Cline comparison centered on a 120B model, which pushes consumer hardware into a very different regime. Once a model spills out of fast memory, your cost is no longer just tokens. You also pay in latency, queueing, and developer patience.<\/p>\n\n\n\n That is why local versus cloud is rarely a purely ideological choice. Cloud can win on convenience and fast startup. Large local systems can win on privacy, predictable marginal cost, and sustained throughput. Consumer hardware can still be the right choice, but often for smaller models that fit cleanly.<\/p>\n\n\n\n ShareAI helps when the best answer is not one backend forever. With 150+ models through one API<\/a>, you can keep a coding workflow stable while changing the model or provider based on the job. That is useful when one task favors low TTFT and another favors stronger sustained output or different pricing.<\/p>\n\n\n\n You can use the ShareAI docs<\/a> and API quickstart<\/a> to keep that routing layer simple. Instead of rewriting your integration every time you want to compare providers or models, you can keep the agent pointed at one API and make smarter backend decisions underneath it.<\/p>\n\n\n\n If you are evaluating inference speed for coding agents, do not stop at one headline number. Measure the opening response, the sustained generation rate, and the operational trade-offs that matter to your team. Then choose a routing layer that lets you adapt as those priorities change.<\/p>\n","protected":false},"excerpt":{"rendered":" A practical look at why time-to-first-token and sustained throughput can produce different winners in AI coding workflows.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"cta-title":"Explore AI Models","cta-description":"Compare price, latency, and availability across providers.","cta-button-text":"Browse Models","cta-button-link":"https:\/\/shareai.now\/models\/?utm_source=blog&utm_medium=content&utm_campaign=inference-speed-for-coding-agents","rank_math_title":"Inference Speed for Coding Agents: TTFT vs Throughput","rank_math_description":"Compare inference speed for coding agents by TTFT, throughput, hardware fit, and routing strategy.","rank_math_focus_keyword":"inference speed for coding agents","footnotes":""},"categories":[6,4],"tags":[66,45,71,70,73,72],"class_list":["post-2886","post","type-post","status-publish","format-standard","hentry","category-insights","category-developers","tag-ai-coding-agents","tag-cline","tag-dgx-spark","tag-inference-speed","tag-local-vs-cloud-inference","tag-ollama"],"_links":{"self":[{"href":"https:\/\/shareai.now\/api\/wp\/v2\/posts\/2886","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/shareai.now\/api\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/shareai.now\/api\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/shareai.now\/api\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/shareai.now\/api\/wp\/v2\/comments?post=2886"}],"version-history":[{"count":2,"href":"https:\/\/shareai.now\/api\/wp\/v2\/posts\/2886\/revisions"}],"predecessor-version":[{"id":2888,"href":"https:\/\/shareai.now\/api\/wp\/v2\/posts\/2886\/revisions\/2888"}],"wp:attachment":[{"href":"https:\/\/shareai.now\/api\/wp\/v2\/media?parent=2886"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/shareai.now\/api\/wp\/v2\/categories?post=2886"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/shareai.now\/api\/wp\/v2\/tags?post=2886"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Why TTFT won the short race<\/h2>\n\n\n\n
Why throughput matters more in real coding sessions<\/h2>\n\n\n\n
Model size changes the economics<\/h2>\n\n\n\n
Where ShareAI fits<\/h2>\n\n\n\n
How to choose the right stack<\/h2>\n\n\n\n
\n
Next step<\/h2>\n\n\n\n